Applications, Attitudes and Ethical Considerations of Generative Artificial Intelligence (Gen AI) In Nursing Education: A Scoping Review.

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Philip Hardie, Andrew Darley, Rosemarie Derwin, Jessica Eustace-Cook, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7537351/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2026 Read the published version in BMC Nursing → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Generative Artificial Intelligence (Gen AI) is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more, based on inputs or prompts. Gen AI technologies are being increasingly integrated into healthcare education, including the field of nursing, where they are utilised to support a range of pedagogical activities. Purpose: This scoping review examined and described the application of Gen AI as a teaching, learning and assessment strategy in Nursing education and examined the ethical implications of and attitudes towards its implementation. Methods: We conducted a scoping review using Arksey and O’Malley’s 5-step framework, as well as the PRISMA-ScR framework guidelines, and searched five databases: EMBASE (Elsevier), Web of Science Core (Clarivate), CINAHL & Medline (EBSCO), Applied Social Science Index and Abstracts, and ERIC (ProQuest). A wide search of grey literature was also conducted. Literature published in English between January 1st 2014, and July 1st 2025 was included in the review. Results: Of the 1,251 articles retrieved, we identified 103 articles for inclusion in the review. There were 44 discussion/opinion/conference papers and 59 empirical research papers. Gen AI has predominantly been used for content creation simulation, personalised learning, tutoring, skill development and assessment. Students and Educators describe mixed attitudes towards the implementation of Gen AI, with several ethical concerns regarding the application of Gen AI in nursing education evident, including privacy, transparency, bias, and accountability issues. Conclusion: While there is growing openness to Gen AI, a body of work remains regarding ethical and educational challenges. Recommendations for educational practice and curriculum development include a need for clear policies and guidelines to ensure the ethical use of Gen AI resources by educators and students. Further research is needed to understand long-term effects and promote responsible implementation within the context of nursing education. Generative Artificial Intelligence (Gen AI) Nursing Education AI Education Educational Technology Scoping Review Figures Figure 1 Introduction Generative Artificial Intelligence (Gen AI) is a rapidly advancing technology that utilises deep learning to produce human-like content in response to complex and varied prompts (Lim et al., 2023 ). Gen AI generates content from Internet-based sources in response to user queries or searches. Gen AI is powered by machine learning and can perform diverse tasks with remarkable efficiency and improved accuracy as the technology continues to develop. These tasks include, but are not limited to, summarising or creating long or short-form content, image editing or designing, video and audio editing, transcribing, and checking lines of code, among other functionalities (Farrelly & Baker, 2023 ). A growing number of Gen AI software tools are available for use, including ChatGPT, OpenAI, Claude, DeepSeek, Google AI, Microsoft Co-Pilot, among others, which are being increasingly incorporated into third-level education. In nursing education, Gen AI is shifting to learner-centred ecosystems that simulate real-world clinical complexity and facilitate personalised curriculum delivery to individual students' needs. Applications include AI-driven virtual patients and immersive scenario simulators that can adapt cases to a student's skill level (Martinez et al., 2025 ). In addition, it provides for the automated generation of realistic clinical notes and assessment questions (Shen et al., 2025 ), intelligent tutoring that offers targeted feedback and remediation (Wang et al., 2024 ), and analytics that predict learners at risk and optimise curricula (Georgieva et al., 2025 ). Gen AI also supports interprofessional teamwork exercises, real-time language translation for diverse cohorts, and streamlines faculty workload by summarising clinical performance and creating customised learning materials, thereby accelerating competency development while preserving patient safety (Alowais et al., 2023 ). Investigating the impact of GenAI is crucial for shaping the future of nursing education, ensuring the profession evolves in line with the rapid advancements in AI technology. While existing scoping and systematic reviews have successfully mapped and evaluated the literature to date on the topic ((Buchanan et al., ( 2021 ); Gerdes et al., ( 2024 ); Gunawan et al., ( 2024 ); Harmon et al., ( 2021 ); Hwang et al., ( 2024 ); Kovalainen et al., ( 2025 ); Labrague & Sabei, ( 2024 ); Lifshits & Rosenberg ( 2024 ); Luo et al., ( 2024 ); Ostick et al., ( 2025 ); Park et al., ( 2024 ); Ramírez‑Baraldes et al. (2025); Seo & Kim, ( 2024 )), the majority have focused on primary empirical studies or specific Gen AI applications, such as ChatGPT. This approach, while valuable, may overlook crucial information found in grey literature, including dissertations, conference papers, and pre-publication manuscripts. The rapid advancement of AI technology often outpaces the review process, potentially leading to outdated findings. Therefore, the omission of such sources may result in an incomplete picture of the rapidly evolving landscape. These limitations collectively underscore the need for a more comprehensive, rigorous, and up-to-date scoping review that incorporates a broader range of evidence sources to better capture the full scope and impact of AI in nursing education, especially given the field's emerging nature and potential for significant future developments. This review makes a novel contribution to the existing evidence synthesis by mapping and examining the range of available information on the attitudes of nurse educators and students towards Gen AI, based on their direct experience, together with the reported ethical considerations of its integration into nursing education. Aim This scoping review aimed to identify current trends and applications of Gen AI as a teaching, learning and assessment tool in nursing education. It also sought to establish potential ethical considerations associated with the use of Gen AI in nursing education and the measures taken to address these within the nursing education environment. By focusing on these critical areas, this review provides a comprehensive understanding of the current state of Gen AI in nursing education, while highlighting areas that require further research and directions for future curriculum development. Methods Study Design This scoping review follows the five-stage review process identified by Arksey and O'Malley ( 2005 ), which was later refined by Levac et al. (2010) and Peters et al. (2015). This methodological framework comprises the following stages: identifying the research question, identifying relevant studies, study selection, charting the data, and collating, summarising, and reporting the results, utilising the reporting scoping reviews—PRISMA ScR extension outlined by Tricco et al. ( 2018 ) & McGowan et al. ( 2020 ). The protocol for this scoping review was registered on the Open Science Framework (OSF) on February 20, 2025 ( https://osf.io/9qe5m/ ). Study Procedure Stage 1: Identify the research questions Following an initial search of the evidence base, the current review was guided by the research question: "What does the literature reveal about the use and attitudes of Gen AI as a teaching, learning and assessment strategy in nursing education, including its associated ethical considerations?". Four research objectives were central to answering this question: To identify the extent and nature of the literature on applying Gen AI in nursing education as a teaching, learning, and assessment strategy. To determine levels of student performance, satisfaction and confidence levels following the utilisation of Gen AI applications in nursing education. To examine the attitudes of nursing students and educators to the application of gen AI as a teaching, learning, and assessment strategy in nursing education. To establish potential ethical considerations associated with the use of Gen AI in nursing education. Stage 2: Identifying Relevant Studies The authors included five healthcare and education-focused databases to search for articles, namely CINAHL Nursing and Allied Health (CINAHL Ultimate), ERIC (Proquest), Medline (EBSCO), Web of Science (Clarivate), and Applied Social Science Index & Abstracts. Peters et al. ( 2020 ) state that a clear scoping review question should incorporate elements of the PCC mnemonic (population, concept, and context). In this instance, the relevant population refers to nursing students, the concept is the application of Gen AI, and the context is the nursing educational environment. The three-step process recommended by the Joanna Briggs Institute was followed, starting with an initial search in EMBASE to derive key terms related to nursing students, Gen AI and nursing education. The search was refined with the support of a Nursing subject expert and a university librarian, and expanded across multiple databases, including grey literature sources. The strategy incorporated Boolean operators, truncation markers, and controlled vocabulary to ensure a thorough capture of relevant literature (Table 1 ). The grey literature search was conducted across six different platforms: OpenGrey (focusing on Humanities and Computer Science subjects), Google Scholar, Google, IEEE Xplore, Overton, ProQuest Dissertations & Theses. The search terms "Generative Artificial Intelligence and Nursing Education" were used consistently. The review included English-language publications from January 1st, 2014, to July 1st, 2025, focusing on Gen AI in nursing education. It included various research methodologies and literature types, while excluding non-English language publications and those examining Gen AI in non-nursing fields. Stage 3: Study Selection Each search was systematically documented, detailing the date, search terms, and results for each search string, as reported by two independent authors. The results were exported to Mendeley ( www.mendeley.com ) for duplicate removal (n = 419 duplicates removed) and then to Covidence ( www.covidence.org ) for screening. The initial search identified n = (1251) articles from the database searches and n = (142) articles from the grey literature search. A pilot test with 50 articles ensured consistent application of inclusion/exclusion criteria amongst the author team. Two reviewers independently screened n = (832) title and abstract and n = (188) full-text records, and conflicts were resolved by a third reviewer. A snowballing approach was also applied to cross-check the selected papers for inclusion against the reference lists of the identified review papers, resulting in the addition of four papers. A total of n = (89) papers were excluded at the full-text stage; n = (37) did not focus on nursing education, n= (13) focused on AI in healthcare, n = (19) were not available in English, n = (7) full-text papers were not available, and n = (13) were previous evidence synthesis papers. A total of n= (103) records were identified for inclusion. A PRISMA flow diagram was used to illustrate the screening process [Figure 1 ]. [Insert Fig. 1 ] Stage 4: Charting the data Using the Joanna Briggs' Institute (2020) data charting documentation, data were extracted from the final set of included records in accordance with the objectives of the review and the research question. Consistent with scoping review methodology, these studies were not appraised for quality. Stage 5: Collating, summarising and reporting the results The extracted data were collated, critically reviewed, summarised descriptively, and prepared into a narrative synthesis by three of the authors. Stage 6: Consultation Exercise As part of the consultation, the results of the narrative synthesis were discussed at a team meeting which was held to review the findings and their application to the review aim. The research team collaboratively derived the conclusions and presented them using tables for clarity and precision. Results Characteristics of selected studies From the n = (103) articles identified, an international representation of the literature is evident, with some studies involving multiple countries (Tables 1 – 3 ). N = (116) countries included including the United States ( n = 40) Australia ( n = 2), China ( n = 8), Egypt ( n = 6), Finland ( n = 1), Greece ( n = 1), Hong Kong ( n = 2), India ( n = 1), Indonesia ( n = 1), Iran ( n = 3), Ireland ( n = 3), Israel ( n = 2), Italy ( n = 2), Japan ( n = 1), Jordan ( n = 2), Korea ( n = 4), Malaysia ( n = 1), Morocco ( n = 3), Philippines ( n = 1), Singapore ( n = 3), Slovenia ( n = 1), Spain ( n = 1), Taiwan ( n = 4), Thailand ( n = 1), Turkey ( n = 10), UAE ( n = 2), Saudi Arabia ( n = 6), United Kingdom ( n = 2). Of these, n = (59) empirical research articles ((Akutay, et.al., ( 2024 ), Athilingam & He ( 2024 ), Basaran & Duru, (2024), Benfatah et.al., ( 2024 ), Bonacaro, et. al. ( 2024 ), Chang, et. al., ( 2022 ), Chang, et. al., ( 2024 ), Dorin & Atkinson ( 2024 ), Fenske & Otts ( 2024 ), Gonzalez-Garcia et al ( 2025 ), Göktaş et.al. ( 2024 ), Han, et.al. ( 2022 ), Hawk, et. al., ( 2024 ), Higashitsuji et.al. ( 2025 ), Jhallad et.al., (2024), Kapadia, et.al. ( 2024 ), Karaçay & Yaşar ( 2025 ), Kong, et. al., ( 2024 ), Kowitlawakul, et.al. (2024), Lane, et. al., ( 2024 ), Lebo & Brown ( 2024 ), Liaw, et. al. ( 2023 ), Makhlouf et.al., ( 2024 ), Miao & Ahn, ( 2023 ), Molu ( 2025 ), Parker, et. al., ( 2023 ), Reed & Dodson (2024), Reeder & Lee (2024), Saatçi, et.al., ( 2024 ), Saban & Dubovi ( 2024 ), Shin et.al., ( 2024 ), Simsek-Cetinkaya, et. al., (2023), Tseng et.al. ( 2025 ), Vaughn et al. ( 2024 ), White, et. al., ( 2024 ), Wolf ( 2023 )); and n = (44) discussion/opinion/conference papers ((Alharbi ( 2024 ), Archibald & Clark ( 2023 ), Bumbach ( 2024 ), Byrne ( 2025 ), Castonguay, et. al. ( 2023 ), Chan, et. al. ( 2023 ), Chen ( 2024 ), Dante, et. al. ( 2022 ), DeGagne ( 2023 ) Degagne et.al. (2024), Lim ( 2023 ), Foronda & Porter ( 2024 ), Gapp et.al. (2025), Gehring, et. al. (2024), Ghane, et., al, (2024), Gonzalez ( 2024 ), Gosak, et. al. ( 2024 ), Harrison ( 2024 ), Irwin et. al. ( 2023 ), Jung ( 2023 ), Kelarijani et.al. ( 2024 ), Lagadec, et. al. ( 2024 ), Liu, et. al., ( 2023 ), Maykut et.al. ( 2024 ), Miao et al., ( 2024 ), Ni et.al. ( 2024 ), O'Connor ( 2021 ), O’Connor (2023), O'Connor, et. al. (2023), O'Connor, et.al. (2024), Pizzulo ( 2024 ), Quattrini et al., ( 2024 ), Reed ( 2023 ), Sharpnack ( 2024 , Shay ( 2023 ), Silvestri-Elmore & Burton ( 2024 ), Simms, ( 2024 ), Simms ( 2025 ), Srinivasan, et. al. ( 2024 ), Sun & Hoelscher ( 2023 ), Topaz et al. ( 2025 ), Thakur et al. ( 2023 )) that explored various applications of Gen AI as a teaching, learning or assessment approach in nursing education. The empirical studies encompass a range of research designs, including quantitative studies such as randomised controlled trials, quasi-experimental studies, and cross-sectional surveys. The qualitative designs included exploratory, phenomenological, descriptive, and mixed-methods studies. A detailed description of the studies is presented in Tables 1 , 2 , and 3 . Insert Table 1 Insert Table 2 Insert Table 3 A narrative account of the review findings regarding the four study objectives is presented below. Objective 1: Teaching, Learning & Assessment Gen AI Applications in Nursing Education Several key themes emerged regarding the integration of Gen AI as a teaching, learning, or assessment strategy in nursing education. These included content creation and enhancement, simulation, personalised learning & tutoring and assessment. In terms of content creation, AI tools like DALL-E3 are being utilised to generate images for educational materials (Akutay et.al., 2024 ), using ChatGPT AI image generation as a strategy to prepare for simulations (Reed & Dodson, 2024) or ChatGPT to create Gen AI art-based therapy materials (Chang et. al., 2024 ), using ChatGPT to summarise lecture slides (Karaçay, & Yaşar,2025), using ChatGPT to summarise research papers (Lane et. al., 2024 ), using ChatGPT to create an AI-based care plan learning strategy (Molu, 2025 ), using ChatGPT, Copilot and Gemini to create patient education leaflets (Saatçi, et.al., 2024 ), using ChatGPT to generate NCLEX-style questions (Miao et al., 2024 ). Simulations and case studies represent a significant emerging theme in the application of Gen AI within nursing education, encompassing both their creation and implementation. Large language models such as ChatGPT and Gemini are increasingly utilised to develop realistic case studies (Higashitsuji et al., 2025 ) and to create interactive patient-nurse dialogue simulations (Kapadia et al., 2024 ). Additionally, AI-powered patient interactions and AI-enabled virtual reality simulations (VRS) have been reported to enhance experiential learning (Jhallad et al., 2024; Simsek-Cetinkaya et al., 2023; Liaw et al., 2023 ; Lebo & Brown, 2024 ; Vaughn et al., 2024 ; White et al., 2024 ). Students are actively encouraged to engage with AI tools, such as ChatGPT, to analyse case studies and explore ethical considerations, further supporting critical thinking and clinical reasoning skills (Göktaş et al., 2024 : Gonzalez-Garcia et al., 2025 ; Shin et al., 2024 ). ChatGPT has also been employed to facilitate individualised debrief sessions for students following a simulation (Benfatah et al., 2024 ). Personalised learning has been enhanced using AI Teaching Assistant Systems (AI-TAS) and chatbots that provide tailored support and address clinical questions, thereby promoting individualised learning experiences (Kowitlawakul et al., 2024; Makhlouf et al., 2024 ; Han et al., 2022 ). Furthermore, conversational chatbots are increasingly employed to assist in teaching nursing skills, offering interactive and real-time guidance to students (Han et al., 2022 ; Gotkas et al., 2024; Hawk et al., 2024 ). In terms of research, Fenske & Otts ( 2024 ) outlined the use of Gen AI in conducting literature reviews, utilising the Gen AI research tool, Elicit. Students found that while Elicit was favoured by 26% for its speed and user-friendliness, it had limitations in accuracy and filtering capabilities, with CINAHL and PubMed preferred by 31.6% and 30.7% of students, respectively, for their specific strengths in nursing research (Fenske & Otts, 2024 ). Several notable applications of AI in assessment have been identified, including AI-powered writing evaluation systems such as IntelliMetric® (Wolf, 2023 ). This technology has also been utilised to support exam preparation, particularly for the NCLEX, by leveraging large language models (Wu et al., 2024 ). Additionally, ChatGPT has been employed in Automatic Writing Evaluation (AWE), demonstrating its potential to provide formative feedback and enhance students' writing skills (Parker et al., 2023 ). The discussion papers largely reinforce Gen AI's potential to enhance learning experiences through personalised tutoring, realistic simulations, and efficient content generation. For instance, Alharbi ( 2024 ) and Archibald & Clark ( 2023 ) emphasise how Gen AI can create tailored scenarios and save educators time by generating essays, exam questions, and automated feedback. Chen ( 2024 ) and DeGagne ( 2023 ) discuss the role of Gen AI in enhancing clinical decision-making and reducing errors by providing instant access to tailored information based on individual students' needs. Similarly, Sharpnack ( 2024 ) and Zhou & Mui ( 2024 ) described how ChatGPT can summarise extensive research, simulate real-life clinical scenarios, support problem-based learning, and enhance student engagement. Gen AI's ability to generate human-like responses and provide instant feedback makes it a valuable resource for both nursing students and their educators (Gehring et al., 2024; Quattrini et al., 2024 ). Gen AI can also support curriculum development and offer immersive learning experiences through virtual reality applications (Topaz et al., 2025 ; Dante et al., 2022 ). Objective 2: Student Performance, Satisfaction and Confidence Levels Following the Use of Gen AI Applications in Nursing Education. Akutay et al. ( 2024 ) found that students using DALL-E3-generated visual narratives achieved notably higher scores in case management (p < 0.05) and on knowledge tests for total hip arthroplasty (t = 2.19, p < 0.05), reflecting improved clinical performance. This study (Akutay et al., 2024 ) and one other study (Saatçi et al., 2024 ) were randomised controlled trials. However, studies undertaken using other designs highlighted notable performance enhancements resulting from the integration of Gen AI in nursing education. Similarly, Gonzalez-Garcia et al. ( 2025 ) reported that students who employed ChatGPT to solve real-world nursing cases experienced substantial academic improvements, with 89.5% noting enhanced performance and a positive correlation between prior ChatGPT use and their GPA. Molu ( 2025 ) demonstrated that a Gen AI-based care plan learning strategy using ChatGPT yielded higher post-test scores in newborn resuscitation compared to conventional methods, indicating positive learning performance following exposure to Gen AI intervention. Wu et al. ( 2024 ) demonstrated that ChatGPT-4 achieved an accuracy rate of 88.67% on NCLEX-RN questions, outperforming previous models and demonstrating effective performance across multiple languages, thereby highlighting its potential as a reliable clinical assessment tool. Benfatah et al. ( 2024 ) observed improvements in critical reflection, engagement, and clinical judgment in students participating in Gen AI-assisted debriefing and virtual simulations. Kapadia et al. ( 2024 ) further supported the efficacy of AI by demonstrating that LLM-generated dialogues within virtual reality environments effectively simulated patient-nurse interactions, with GPT-3.5 outperforming in health history scenarios and supporting realistic skill development. Rao et al. (2024) also noted that Gen AI-driven automated essay scoring led to significant gains in students' writing proficiency across multiple domains. Tseng et al. ( 2025 ) in Taiwan found that an 18-week AI literacy program led to marked improvements in students' report-writing abilities, as evidenced by higher assessment scores and robust evaluation metrics. Finally, Wolf ( 2023 ) noted significant improvements in student confidence and writing skills following the use of an AI-powered writing assessment tool. The empirical studies indicate a mixed but generally favourable impact of Gen AI interventions on nursing students' satisfaction and confidence across differing contexts and applications. Akutay et al. ( 2024 ) found that using DALL‑E3 for image generation demonstrated significantly higher case management performance in the AI group (p 0.05), suggesting that performance gains do not necessarily translate into greater reported satisfaction. Similarly, Başaran and Duru ( 2024 ) found that students taught with ChatGPT reported the highest confidence in managing sexual health issues (83.9%), yet satisfaction did not differ significantly from Kahoot or traditional methods. In contrast, Benfatah et al. ( 2024 ) reported high student ratings for ChatGPT's accessibility (4.2), engagement (4.4) and usefulness (4.1), signalling strong satisfaction with ChatGPT as a virtual patient simulation tool despite some need for additional support. Karaçay et al. (2024) observed increased knowledge, critical thinking and satisfaction linked to ChatGPT classroom activities. Simsek‑Cetinkaya et al. (2023) reported higher satisfaction for AI‑assisted screen‑based simulation versus standard patient simulation (p < 0.05); and Reed & Dodson (2024) found reduced anxiety, enhanced preparatory knowledge and stronger emotional engagement when AI‑generated patient backstories were used as a pre‑simulation strategy. Conversely, Lebo and Brown ( 2024 ), Lane et al. ( 2024 ), Başaran & Duru ( 2024 ), and Moskovich & Rozani ( 2025 ) highlighted students' positive perceptions, citing benefits such as accessibility, engagement, and usefulness. However, concerns about technical issues, limited engagement, and the need for human oversight were also reported. In terms of confidence specifically, Akutay et al. ( 2024 ), Tural & Doymaz (2024), and Lebo & Brown ( 2024 ) found that students using Gen AI tools, including ChatGPT and AI‑based simulations, reported increased confidence in clinical or educational skills, with Lebo & Brown ( 2024 ) noting improved familiarity with patient interactions and Tural & Doymaz (2024) identifying enhanced self‑efficacy following Gen AI‑focused interventions. Objective 3: Educator and Student Attitudes towards Gen AI in Nursing Education Several studies specifically examined students' and educators' attitudes, based on their direct engagement, toward Gen AI in nursing education (Abdel-Moaty et.al., 2024 ), Ahmed et.al. (2024), Alenazi ( 2025 ), Bouriami, et. al., ( 2024 ), El-Sayed et.al., ( 2024 ), El-Sayed et.al., (2025), Kilci Erciyas et.al., ( 2024 ), Gunawan, et. al., ( 2024b ), Hashish & Alnajjar, (2024), Issa et.al., ( 2024 ), Kang, et. al., ( 2023 ), Labrague et.al., ( 2023 ), Liu, et al., ( 2024 , Luo et.al., ( 2023 ), Ma, et. al., ( 2025 ), Migdadi et.al., ( 2024 ), Moskavich & Rozani, (2025), Sumengen, et al., ( 2025 ), Yalcinkaya et al., ( 2024 ), Yang, ( 2024 ), Yigit & Acikgoz ( 2024 ) (Table 3 ). These studies have primarily utilised cross-sectional designs with structured questionnaires to investigate factors including AI literacy, acceptance, and ethical awareness. Among the tools used were the Artificial Intelligence Anxiety Scale (AIAS) (Yigit & Acikgoz, 2024 ), the General Attitudes Toward Artificial Intelligence Scale (GAAIS), the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) (Yalcinkaya et al., 2024 ), and the Artificial Intelligence Literacy Scale (AILS). Other relevant instruments include the Test for AI Ethical Awareness (TAIEA) (Migdadi et al., 2024 ), the AIQ–Self-Regulation–College Student Employability questionnaire (Luo et al., 2023 ), the Extended Technology Acceptance Model (ETAM) (Kang et al., 2023 ), and the Individual Innovativeness Scale (IIS). The GAAIS has also been referenced in studies by Kilci Erciyas et.al. ( 2024 ), along with the Innovative Thinking Competencies Scale (ITCS), the Scale for the Assessment of Nonexperts’ AI Literacy (SNAIL), and the Career and Talent Development Self-Efficacy Scale (CTDSES) (El-Sayed et al., 2025). Additionally, the Unified Theory of Acceptance and Use of Technology (UTAUT) (Alenazi, 2025 ) has been employed. Several researchers, including Abdel-Moat et al. (2024), Bouriami et al. ( 2024 ), Hashish & Alnajjar (2024), Issa et al. ( 2024 ), Labrague et al. ( 2023 ), Liu et al. ( 2024 ), and Moskavich & Rozani (2025), developed their own questionnaires based on key themes identified from the literature. Qualitative methods, such as interviews and focus groups, were utilised to gain deeper insights into students’ experiences and perceptions of Gen AI tools (Ahmed et al., 2024; Gunawan et al., 2024b ; Ma et al., 2025 ). See Table 3 for full details. Overall, there is a generally positive outlook towards the use of Gen AI tools, such as ChatGPT and other large language models (LLMs), in enhancing nursing education. Alenazi ( 2025 ) and El-Sayed et al. ( 2024 ) highlight that nursing students perceive Gen AI as a valuable tool that can improve learning outcomes and academic performance. Alenazi ( 2025 ) identified that the perceived usefulness and ease of use significantly influence students’ willingness to adopt Gen AI. Similarly, Hashish & Alnajjar (2024), Sumengen et al. ( 2025 ) note that increased AI literacy correlates with greater acceptance and readiness to use Gen AI. However, Bouriami et al. ( 2024 ) found that while nurse educators generally accept ChatGPT, concerns remain regarding plagiarism, with educators who do not view it as a risk being more likely to use the tool. Other concerns outlined by educators were the quality and robustness of outputs from ChatGPT. For example, Reeder & Lee (2024) reported ChatGPT failed to produce responses that would constitute a pass grade when tested in written assessment questions in a nursing informatics programme; however, it could pass three of four assignments from a data science programme. Moreover, Gunawan et al. ( 2024 ) report that while Indonesian nursing students see ChatGPT as helpful, they also view it as untrustworthy and emphasise the need for verification of Gen AI-generated information. Objective 4: Ethical Considerations of Gen AI Implementation in Nursing Education Several ethical concerns regarding the application of Gen AI in nursing education were evident in the literature. These concerns include issues related to privacy and data security, transparency, bias, academic integrity, human oversight and accountability. The discussion of ethical concerns was at the forefront of the vast majority of discursive papers ((Archibald & Clark ( 2023 ), Bumbach ( 2024 ), Byrne ( 2025 ), Castonguay, et. al. ( 2023 ), Chan, et. al. ( 2023 ), Chen ( 2024 ), DeGagne ( 2023 ) Degagne et.al. (2024), Lim ( 2023 ), Foronda & Porter ( 2024 ), Gapp et.al. (2025), Gehring, et. al. (2024), Ghane, et., al, (2024), Gonzalez ( 2024 ), Gosak, et. al. ( 2024 ), Harrison ( 2024 ), Irwin et. al. ( 2023 ), Jung ( 2023 ), Kelarijani et. al. ( 2024 ), Lagadec, et. al. ( 2024 ), Liu, et. al., ( 2023 ), Maykut et.al. ( 2024 ), Miao, et. al., (2023), Ni et. al. ( 2024 ), O'Connor ( 2021 ), O’Connor (2023), O'Connor, et. al. (2023), O'Connor, et. al. (2024), Pizzulo ( 2024 ), Quattrini et al. ( 2024 ), Reed ( 2023 ), Sharpnack ( 2024 , Shay ( 2023 ), Simms, ( 2024 ), Simms ( 2025 ), Srinivasan, et. al. ( 2024 ), Sun, et. al., (2023), Topaz et al. ( 2025 ), Thakur et al. ( 2023 )) indicating it is a primary concern for the implementation of Gen AI in Nursing education, with all but (Alharbi ( 2024 ); Dante, et. al. ( 2022 ); Silvestri-Elmore & Burton ( 2024 )) outlining any ethical concerns. Benfatah et al. ( 2024 ) emphasise the importance of safeguarding student privacy and ensuring transparency in data collection and usage. Similarly, Gotkas et al. (2024) emphasise the importance of avoiding the use of confidential student or patient data when prompting Gen AI. This consideration is vital for protecting confidentiality and preventing discrimination or bias in the generated data, which is crucial for maintaining trust and fairness in Gen AI-driven educational tools. Benfatah et al. ( 2024 ), Gonzalez ( 2024 ), and Srinivasan et al. ( 2024 ) note that Gen AI systems can introduce biases stemming from incomplete or skewed data, which can lead to educational inequities. "Hallucinations" result in false or misleading information, raising concerns about the use of Gen AI in terms of academic integrity (Foronda & Porter, 2024 ; Liaw et. al., 2023 ; Simms, 2025 ). Developing students' awareness of Gen AI's limitations is crucial to ensure the responsible and effective integration of this technology (Topaz et al., 2025 ). Another significant ethical issue is the potential impact on interpersonal relationships, such as those between nurses and patients or students and educators. Bonacaro et al. ( 2024 ) caution that overreliance on Gen AI could diminish direct human interaction, potentially weakening the empathy and interpersonal skills essential for nursing practice. Abdulai et al. (2023) also note that ChatGPT's inability to ensure confidentiality and its reductionist approach could undermine the holistic and empathy-driven care model that is central to nursing. Ghane et al. (2024) and Sharpnack ( 2024 ) emphasise the importance of educational training for nurses, educators, and students on the ethical use of AI tools, advocating for robust protections to safeguard sensitive information and maintain confidentiality. Researchers (Archibald & Clark, 2023 ; Athilingam et al., 2024; Dorin & Atkinson, 2024 ; and Lagadec et al., 2024 ) discuss the risks of academic dishonesty, including plagiarism and fraud associated with Gen AI-generated content, which could undermine the integrity of nursing education. Furthermore, Hawk et al. ( 2024 ), Simms ( 2025 ), and Topaz et al. ( 2025 ) emphasise the importance of human oversight and critical evaluation of Gen AI-generated information to prevent inaccuracies, ensuring that Gen AI supplements rather than replaces traditional teaching methods. This is supported by Chen ( 2024 ), who cautions against excessive reliance on Gen AI, as it may hinder students' critical thinking and problem-solving abilities, leading to a decline in independent thought. In terms of assessment, several discussion pieces discussed the potential misuse of Gen AI in particular for assessment methods that rely on text based responses ((Byrne ( 2025 ), Castonguay et al. ( 2023 ), Irwin et al. ( 2023 ), Lagadec et al. ( 2024 ), Miao et al. (2023), Ni et al. ( 2024 ), O'Connor (2023), Quattrini et al. ( 2024 ), Reed ( 2023 ), Shay ( 2023 ), Topaz et al. ( 2025 ), and Thakur et al. ( 2023 )), highlighting the need to reframe assessment strategies to from traditional text-based methods to more hands-on, practical evaluations that protect against the potential misuse of gen AI by focusing on competency based clinical skills, performance-based tasks, problem based learning, that require critical thinking and personal judgment and the increased incorporation of oral assessments, including oral presentations, debates, and mini-viva, was also discussed. Researchers (Archibald & Clark ( 2023 ), Benfatah et.al. ( 2024 ), Lane et. al. ( 2024 ), Lagadec et. al. ( 2024 ), and Simms ( 2025 )) emphasise the importance of developing comprehensive institutional policies that guide the ethical application of Gen AI tools, while advocating for staff training to mitigate potential issues related to academic integrity. O'Connor et al. (2023) further emphasise the need for transparent policies that foster open discussions on topics such as plagiarism and the responsible use of AI detection tools to educate students about the responsible use of Gen AI and the consequences of misuse in terms of academic misconduct. Shay ( 2023 ) argues the importance of ensuring transparency and accountability in the integration of Gen AI in nursing education, advocating for transparency through the disclosure of AI tool usage in assignments to promote accountability. Furthermore, Archibald & Clark ( 2023 ), Alghamdi et al. ( 2024 ), Benfatah et.al. ( 2024 ), Lane et. al. ( 2024 ), Lagadec et. al. ( 2024 ), and Simms ( 2025 ) argue that institutional policies must move beyond punitive policy models, instead promoting transparency and accountability to encourage a culture of trust and ethical behaviour, which reduces the temptation for dishonest practices. Concurrently, Sun et al. (2023) and DeGagne ( 2023 ) argue that promoting critical thinking and ethical awareness plays a pivotal role in combating over-reliance on Gen AI. Yang ( 2024 ) and Migdadi et al. ( 2024 ) emphasise the importance of integrating AI ethics into nursing curricula to address concerns and ensure its responsible use. Ahmed et al. (2024) and El-Sayed et al. (2025) emphasise the need for more comprehensive training and exposure to Gen AI tools to enhance digital literacy and confidence among nursing students. Sun et al. (2023) propose assignments encouraging self-reflection and independent learning, emphasising tasks that require critical evaluation of Gen AI-generated content. DeGagne ( 2023 ) similarly emphasises the importance of values clarification in preparing nursing students for AI's ethical challenges, ultimately fostering ethical awareness and the development of critical thinking skills necessary for navigating AI in healthcare. Srinivasan et al. ( 2024 ) recommend involving diverse stakeholders in the development of Gen AI systems to align with ethical and social values in nursing. Addressing bias, as noted by Srinivasan et al. ( 2024 ), entails training AI systems on diverse datasets to support an equitable educational experience. Lim ( 2023 ) warns against linguistic bias, advocating for inclusivity in access to Gen AI tools, especially for non-native English speakers. Furthermore, Park et al. ( 2024 ) argue that educators must be conscious of the equitable access and cost of Gen AI tools, as well as the variance in outputs observed between paid and free versions of platforms such as ChatGPT, to avoid educational inequalities. In parallel with these initiatives, Simms ( 2025 ) and Topaz et al. ( 2025 ) stress the critical role of human oversight in ensuring that the use of Gen AI in nursing education enhances, rather than replaces conventional teaching approaches and advocate for the establishment of ethical frameworks to steer this integration, as highlighted by DeGagne et al. (2024). Discussion This scoping review aimed to explore the use of Gen AI as a teaching, learning, and assessment strategy in nursing education and to investigate the ethical considerations and perceptions associated with its implementation. The findings highlight the exponential growth and wide range of applications, including content creation, simulation, personalised learning and tutoring, and assessment preparation and implementation. The review findings are consistent with those observed in the education practices of other allied healthcare disciplines, including medicine (Rincón et al., 2025 ), pharmacy (Mortlock & Lucas, 2024 ), and physiotherapy (Lindbäck et al., 2025 ), reporting an exponential growth on the implementation of Gen AI pedagogical implementation, indicating the universal investment in and impact of Gen AI on healthcare education. The current body of empirical literature on Gen AI in nursing education has predominantly focused on the application of ChatGPT. However, there is a pressing need for further research to explore other Gen AI applications, such as Claude, DeepSeek, Google AI, and Microsoft Co-Pilot, to develop a richer empirical understanding of the available tools and their potential pedagogical impact on nursing education. A study by Fenske & Otts ( 2024 ) investigated the implementation of Elicit ( https://elicit.com/ ) , with particular emphasis on its accuracy, relevance, and efficiency using nursing-specific filters in conducting a literature review. While the results revealed some inaccuracies in identifying relevant papers and limitations in filtering capabilities, it is noteworthy that 26% of students preferred Elicit over traditional search databases such as PubMed and CINAHL, citing its speed and ease of use as primary advantages. The authors acknowledge the emergence of specialised Gen AI tools designed to support literature review and discovery processes. These include Scite, Research Rabbit, and Semantic Scholar, which offer advanced features such as citation context analysis, networked discovery, and AI-assisted synthesis of research findings. It is imperative that future research focuses on evaluating the accuracy, potential biases, utility, and reproducibility of these platforms. Additionally, comparative studies should be conducted to assess their performance and impact on research workflows in relation to traditional database searches and more general-purpose AI models, such as ChatGPT. In this review several papers (Byrne ( 2025 ), Castonguay et al. ( 2023 ), Irwin et al. ( 2023 ), Lagadec et al. ( 2024 ), Miao et al. (2023), Ni et al. ( 2024 ), O'Connor (2023), Quattrini et al., ( 2024 ), Reed ( 2023 ), Shay ( 2023 ), Topaz et al. ( 2025 ), and Thakur et al. ( 2023 )), discussed the onus on nurse educators to reframe traditional assessment strategies that rely on text based methods to competency-based clinical skills, performance tasks, problem-based learning, and increased use of oral assessments like presentations, debates, and mini-vivas to safeguard against the misuse of Gen AI in formal summative assessments. While the wider literature also recommends this approach (Le, 2024 ; Nikolopoulou, 2025 ; Uanachain & Aouad, 2025 ), others argue that the use of Gen AI should be incorporated in assessment strategies, but most importantly, the disclosure of the use of Gen AI is paramount (Cotton et al., 2025 ; Khlaif, 2025 ; Xia et al., 2024 ). One such approach is the incorporation of the Gen AI Assessment Scale (Perkins et al., 2024 ), which outlines five levels of AI integration in assessments, ranging from no AI use to full AI collaboration. It describes how AI can be utilised for idea generation, editing, task completion, and as a co-pilot, with varying requirements for citation and disclosure of AI-generated content across the different levels. However, for faculty to design assessments in the context of AI, it is imperative that they first familiarise themselves with AI tools and their capabilities (Ng & Phua, 2025 ). This review highlights that research on the impact of integrating Gen AI tools on student performance, confidence, and satisfaction is promising, yet limited. The studies included suggest that the use of Gen AI in nursing education generally leads to significant improvements in student performance, confidence and satisfaction. Notable enhancements have been observed in areas such as case management, knowledge assessments and simulation-based learning. These findings are consistent with those of Pham et al. ( 2025 ), who recently conducted a systematic review examining the impact of Gen AI on health professional education in relation to student learning. This review found that Gen AI has a positive impact on various aspects of student learning, including knowledge acquisition, inquiry, practice, and production, through various pedagogical approaches that incorporate Gen AI. These approaches clarify concepts, support rapid inquiry, simulate clinical practice, facilitate academic production, and foster discussion and collaboration. However, the impact on student performance, confidence, and satisfaction can vary depending on the specific Gen AI tools used and the context. Many identified studies also underlined concerns about technical issues and the need for educator oversight to improve students' performance through the ease of use and effectiveness of Gen AI systems. However, Holzinger et al. ( 2025 ) argue that the future of human oversight in AI demands interdisciplinary technical and policy reform, embedding explainability and human-in-the-loop design, developing scalable monitoring and validation tools, harmonising international governance and standards, and investing in training while prioritising equity, transparency, and accountability to ensure responsible, trustworthy AI systems. The need for clear institutional policies, comprehensive training, stakeholder engagement, and accountability measures, including the disclosure of AI use, is expressed to guide the responsible and equitable deployment of AI tools in nursing education. A recent systematic review (García-López & Trujillo-Liñán, 2025 ) highlighted the need to design inclusive regulatory frameworks, enhance digital literacy, and integrate Gen AI tools with constructivist and self-directed learning models. Based on the available evidence, the authors believe that this will ultimately improve student satisfaction and confidence in the use of Gen AI systems. From an ethical perspective, several considerations emerged from the identified evidence regarding the integration of Gen AI in nursing education. Key considerations include ensuring privacy and transparency in data collection and AI use, in addition to preventing and mitigating biases and disparities in access or language by utilising diverse datasets and inclusive policies. The importance of human oversight and critical evaluation in managing and responding to AI-generated hallucinations and inaccuracies cannot be overemphasised to help preserve academic integrity. Furthermore, the importance of avoiding overreliance on AI is outlined, as it could undermine empathy, interpersonal skills, and independent critical thinking. The development and implementation of Gen AI in nursing education presents challenges that must be addressed through well-defined ethical frameworks. A systematic review by Fu & Weng ( 2024 ) identified key ethical principles for framing responsible, human-centred AI practices in education, including (1) Fairness & Equity, (2) Privacy and Security, (3) Agency & Autonomy, (4) Transparency & Intelligibility, and (5) Non-maleficence & Beneficence. Additionally, Cherner et al. ( 2025 ) have developed an ethical decision tree for introducing Gen AI in higher education based on established international frameworks and guidelines. Educators are advised to (1) identify their needs, (2) learn about the tool’s capabilities, terms, and training data, followed by (3) a Layer 1 yes/no assessment of local and societal impacts, and (4) a Layer 2 review of disclosures, policies, and outputs, considering contextual factors. Cherner et al. ( 2025 ) also recommend treating policy and AI literacy as contextual supports rather than evaluative criteria when implementing Gen AI. Developing AI literacy among nursing students is crucial for addressing educational inequalities (Hoelscher & Pugh, 2025 ). AI literacy helps level the playing field by equipping students from diverse backgrounds with the skills needed to access, utilise, and benefit from AI tools, ensuring that advantages are not confined to those with prior technology exposure or resources. It also enables a critical assessment of bias, inaccuracies, and ethical privacy risks, while preserving clinical reasoning and empowering students to advocate for equitable institutional AI policies (Simms, 2025 ). Although many higher education institutions worldwide have implemented AI policies to address concerns over academic integrity and provide governance for the ethical use of AI in teaching and assessment, there is still much to be learned as technology unfolds and how it can be effectively valued in the nursing education context. Knowledge Gaps in Current Evidence-Base While interest in integrating Gen AI into nursing education is on the rise, empirical research in this area remains scarce. Many discussion papers explore potential applications; only a small number of empirical studies assess their direct impact on learning outcomes, and few of these are randomised controlled trials. Further empirical research is needed to evaluate the effectiveness of these policies in preventing misconduct, shaping student behaviour, and ensuring equitable educational outcomes. A notable limitation of the research to date in the field is its reliance on quantitative surveys and self-reported data, which can introduce bias and make it challenging to draw causal conclusions about the effectiveness of Gen AI in enhancing student performance. Additionally, the small sample sizes of these studies further restrict the generalizability of their findings. Moreover, much of the literature prioritises students' perceptions over measurable academic or clinical competencies, offering limited insight into how Gen AI can effectively enhance practical nursing education. Although Gen AI is rapidly emerging in nursing education, the evidence base remains early and fragmentary. To date, there are no robust longitudinal studies, and this scoping review did not identify any current postgraduate research projects (PhD or Master's level). This may be because PhD programs typically take 4–6 years to complete, and the technology is still relatively new. It is likely that empirical research is limited because the formal adoption of Gen AI and its potential risks to assessment integrity are still emerging challenges for educational institutions worldwide. Educators are predominantly awaiting further research and real-world case studies to better understand whether and how Gen AI influences academic integrity and overall educational outcomes. Strengths and Limitations of the Scoping Review This scoping review has several strengths and limitations. The literature search was conducted in collaboration with a subject librarian, who utilised several databases and extensive grey literature sources. The review is the first of its kind to include the broader breadth of grey literature in conjunction with empirical research. The review only included publications written in English, which potentially excluded relevant studies published in other languages. The greater focus was on published literature, which may have introduced a risk of publication bias. Additionally, the review excluded studies on Gen AI applications outside the field of nursing education, potentially missing important interdisciplinary insights and transferable findings from other fields in healthcare education. Furthermore, given the rapid development of Gen AI, some recent advances or ongoing discussions may not be fully represented in this review. These limitations should be considered when interpreting the findings, emphasising the need for ongoing research in this evolving field. Conclusion While Gen AI is transforming nursing education by offering innovative tools for teaching, learning, and assessment, this review offers a novel mapping of the attitudes towards and ethical considerations of Gen AI, which include privacy, bias, academic integrity, and the potential for dependency. These may be addressed through clear institutional policies, comprehensive training and educator oversight. Future research should focus on evaluating the long-term impact of Gen AI on nursing education, developing robust ethical frameworks, and ensuring equitable access to AI tools and resources. Finally, prioritising AI literacy among students and educators is essential, as it fosters critical thinking and ethical awareness to navigate the complexities of AI integration. Abbreviations Generative Artificial Intelligence: Gen AI Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Funding: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors Authors' contributions This scoping review was developed with contributions from all authors as detailed below: Conceptualisation and Design: {PH, AD, RD, JEC, SK, BMB, MM} were responsible for the initial conceptualisation and design of the study. Methodology: {PH, AD, RD, JEC, SK, BMB, MM} developed the methodology and the review criteria. Literature Search and Data Extraction: {PH, JEC, AS, DZ} conducted the initial literature search and were responsible for data extraction and management. Drafting of the Manuscript: {PH, MM} drafted the first version of the manuscript. All authors provided substantial revisions and improvements. Critical Review and Editing: {PH, AD, RD, JEC, SK, BMB, MM, AS, DZ} critically reviewed the manuscript for important intellectual content and provided necessary revisions. All authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work. PH is the guarantor. Acknowledgements: Not Applicable References Abdel-Moaty, M. A. A., El-Molla, M. A., & Abdel-Wahab, E. A. (2024). Nursing interns’ perception about artificial intelligence applications in nursing. Egyptian Nursing Journal, 21(2), 121–128. https://doi.org/10.4103/enj.enj_19_24 Abdulai, A. F., & Hung, L. (2023). 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Journal of clinical nursing, 33(6), 2362–2363. https://doi.org/10.1111/jocn.17089 Tables Table 1 Keywords for each search string & database Population (P): Nursing students EMBASE: 'nursing student'/exp Medline (MH "Students, Nursing") OR Web of Science topic search with keywords CINAHL: (MH "Students, Nursing+") OR (MH "Students, Pre-Nursing") OR (MH "Students, Nursing, Baccalaureate+") OR (MH "Students, Nursing, Practical") Applied Social Science index and abstract: MAINSUBJECT.EXACT.EXPLODE("Nurses") AND MAINSUBJECT.EXACT.EXPLODE("Undergraduate students") ERIC: MAINSUBJECT.EXACT.EXPLODE("Nursing Students") Keywords on title and abstract: “Student Nurs*” OR “Trainee Nurs*” OR “Nursing Trainee*” OR “Nurs* Intern*” OR “Nursing Learner*” OR “Novice Nurse*” OR “Undergraduate Nurse*” OR “Postgraduate Nurse*” OR “Nursing student*” OR “baccalu* nurs*” OR “nursing novice*” OR “nursing mentee*” (AND) Concept (C) : AI EMBASE: 'artificial intelligence chatbot'/exp OR 'virtual assistant'/exp OR 'artificial intelligence'/exp Medline: (MH "Artificial Intelligence+") OR (MH "Machine Learning+") CINAHL: (MH "Artificial Intelligence+") OR (MH "Artificial Intelligence, Generative") Applied Social Science Index & Abstracts: MAINSUBJECT.EXACT.EXPLODE("Artificial intelligence") Web of Science: topic search on keywords ERIC: MAINSUBJECT.EXACT.EXPLODE("Artificial Intelligence") Keywords on title and abstract : “Generative Artificial Intelligence” OR “Gen AI” OR “Artificial Intelligence” OR “Computational Intelligence” OR “Automated Reasoning” OR “Algorithmic Intelligence” OR ”Synthetic Intelligence” OR “Machine Learning” OR ChatGPT OR Chatbot* OR “Conversational AI” OR “AI Assistant*” OR “Virtual Assistant*” OR “Intelligent Chatbot” OR “Automated Chat Agent*” OR avatar OR “Virtual Chat Companion*” OR “Dialogue System” OR “large language model*” OR “Learning Management System*” OR “AI-Powered” OR “Notebook LM” OR “Google AI” OR “AI” OR “sentient analys*” OR Grammarly OR Duolingo OR “Microsoft Co-Pilot” OR Scite OR “Research Rabbit” OR “Semantic Scholar” (AND) Context (Co) : Nursing Education EMBASE: ('nursing education'/exp OR 'nurse training'/exp OR 'pedagogics'/exp) Medline: (MH "Education, Nursing") OR (MH "Education, Nursing, Baccalaureate") OR (MH "Preceptorship") CINAHL: (MH "Education, Nursing") OR (MH "Nurse Educators") OR (MH "Education, Nursing, Baccalaureate+") OR (MH "Education, Nursing, Practical") OR (MH "Entry Into Practice") OR (MH "Internship and Residency") Applied Social Science Index & Abstracts: no appropriate index terms Web of Science: topic search on keywords ERIC: MAINSUBJECT.EXACT.EXPLODE("Nursing Education") Keywords on title and abstract : “Nursing Educat*” OR “Nursing Train*” OR “Nursing Studies” OR “Nursing Curricul*” OR “Nursing Pedagog*” OR “Nursing exam*” OR “Nursing Teaching*” OR “Undergraduate Nurs*” OR “Post-Graduate Nurs*” OR “PG Nurs*” OR “UG Nurs*” OR “postgraduate nurs*” Table 1: Discussion/Opinion/Conference Papers Examining Teaching, Learning and Assessment Applications of Gen AI in Nursing Education Author, Year of Publication Location Publication Type: Gen AI Teaching Learning Assessment Applications Ethical Considerations of the Application of Gen AI in Nursing Education Abdulai, et. al. (2023) Canada Commentary Discuss how the use of ChatGPT may undermine the values, principles, and core assumptions that underpin nursing research, education, and practice. ChatGPT's inability to ensure confidentiality and its reductionist approach pose significant challenges to nursing's holistic, empathy-driven care model and ethical responsibilities. Raises concerns about academic integrity and the potential development of nurses lacking critical thinking skills and core nursing values. Alharbi (2024) Saudi Arabia Discussion Gen AI can enhance simulation experiences by offering tailored, realistic scenarios, such as AI-driven robots that provide more authentic interactions than traditional simulators. Did not discuss ethical considerations. Archibald & Clark (2023) Canada Discussion ChatGPT enhances education by generating diverse content, creating interactive tutors, automating grading, and personalising online learning experiences, saving time for educators while improving effectiveness for students. Educators play a vital role in promoting ethical conduct and academic integrity among students. Institutions, in turn, must provide AI ethics training, establish clear policies, and implement ongoing risk assessment strategies to ensure the responsible integration of ChatGPT in education. Bumbach (2024) USA Discussion ChatGPT chatbot and retrieve an output. It can be used for answering questions, searching for information, generating essays, and articles in a conversation between the user and the program. Educators can resist AI or, alternatively, proactively incorporate this technology while employing it with evidence-based information, ethics, morals, and a professional approach to its use. Byrne (2025) USA Discussion Artificial Intelligence building and delivering an educational experience requires careful matching of learning objectives with teaching/learning activities. Difficulty in detecting academic misconduct should not overshadow subtle or embedded biases. Bias in data used for training or design in an AI system may disenfranchise certain students. Castonguay, et. al. (2023) Canada Discussion ChatGPT is an AI-enabled text generator that can engage in conversations and answer questions. Gen AI has limitations in terms of the accuracy of its outputs and potential biases Chan, et. al. (2023) China Discussion ChatGPT can generate interview questions and nursing interventions. ChatGPT should reflect an emphasis on the importance of nursing actions and incorporate a holistic and humanistic approach. Chen (2024) Taiwan Discussion ChatGPT enhances education through personalised learning, user-friendly interfaces, quick information access, writing and problem-solving assistance, and supports educators in curriculum development by generating teaching cases and simulating clinical scenarios. Gen AI may limit students’ critical thinking, problem-solving, and innovation capabilities, leading to a lack of independent thought. Dante, et. al. (2022) Italy, USA Discussion Gen AI can be utilised to programme High-Fidelity Patient Simulators (HFPS) Did not discuss ethical considerations. DeGagne (2023) USA Discussion Advanced technologies like predictive analytics, VR simulations, computer vision, and natural language processing enhance medical data analysis, reduce errors, improve patient care, and offer interactive learning tools like virtual avatars and chatbots to enrich nursing education. Early integration of values clarification in nursing education prepares students to address AI-related ethical challenges in healthcare, fostering critical thinking and ethical awareness around issues like privacy, equity, and patient-centered care. Degagne et.al. (2024) USA, Korea Discussion AI, Turnitin32 or GPTZero. Gen AI can provide realistic simulations of clinical scenarios to help students practice clinical decision-making skills in a secure environment. Proposes five ethical principles (autonomy, nonmaleficence, beneficence, justice, and explicability) as a framework for responsibly integrating Gen AI in nursing education, addressing ethical challenges to protect students, maintain standards, and promote patient-centred care. Lim (2023) USA Editorial Discussion Machine-generated writing tools like ChatGPT can be utilised in education for student tutoring, stimulating dialogue, and as memory aids. There may be a bias associated with ChatGPT, as students with English as an additional language might be more likely to be suspected of using AI. Foronda & Porter (2024) USA Discussion ChatGPT. AI involves computer programs that simulate qualities of the human mind, such as the ability to process language, recognise pictures, solve problems, and learn. Caution over the use of AI can lead to bias, injustice, inequities, and inaccuracies. Gapp et.al. (2025) USA Discussion Open artificial intelligence (AI) platforms in nursing education can be beneficial when given the correct prompts to use. Did not discuss ethical considerations. Gehring, et. al. (2024) USA Discussion AI, ChatGPT, Generative Artificial Intelligence, Large Language Models. ChatGPT 4 produces more accurate written essays with citations, provides correct answers to open-ended questions, presents valid research data, and achieves improved pass rates on professional competency exams. Academic nurse educators should consider incorporating GenAI tools into their educational practices. Some are willing, while others have concerns over ethical implications, such as academic integrity. Ghane, et., al, (2024) Iran Editorial Discussion Artificial Intelligence (AI). A primary application of AI in nursing is patient monitoring. AI‐powered monitoring systems can continuously collect and analyse patient data. Data privacy and security must be maintained, and nurses need adequate training to use AI tools effectively. There needs to be a balance between human interaction and AI use in hospitals. Gonzalez (2024) USA Discussion ChatGPT, a conversational AI trained on diverse text, generates human-like, context-aware responses and can enhance nursing education by supporting teaching, personalised learning, and critical thinking. Concerns over potential biases, user privacy, transparency, and psychosocial considerations. It is important for nurse educators considering the use of AI technology in curricula to have awareness of these concerns and possible actions to mitigate them in practice Gosak, et. al. (2024) USA, Slovenia & UK Discussion ChatGPT. Chatbots can be utilised in problem-based learning to provide nurses with practical experience. ChatGPT can assist nurses with patient problems, medical histories, and care planning, but its outputs may not align with the North American Nursing Diagnosis Association-International (NANDA-I), highlighting the need for professional oversight. Harrison (2024) UK Discussion ChatGPT is a chatbot that responds to questions by reviewing vast quantities of data previously generated by humans. Concerns over privacy, security, lack of accountability for errors. Also, exacerbates digital inequalities and there is a loss of human interaction. Irwin et. al. (2023) Australia Editorial Discussion ChatGPT is trained on large amounts of text data and can generate human-like content in response to user prompts with high levels of accuracy. Assessment methods that rely on text-based responses prepared and uploaded individually by the student may compound academic integrity concerns. Jung (2023) Korea Discussion Artificial Intelligence (AI) can provide improved realism, engagement, and personalisation in nursing simulation. Concerns over the use of AI include autonomy and patient privacy. AI may redefine educators' roles and may necessitate them to teach students ethical decision-making. Kelarijani et.al. (2024) Iran Discussion ChatGPT from OpenAI provides comprehensive, logical textual responses to assist nursing students and instructors with academic questions, assignments, and research projects. Reliance on the use of AI can lead to problems in developing essential skills in nursing students. These skills include critical thinking, clinical reasoning, the ability to design a nursing care plan, and problem-solving skills Lagadec, et. al. (2024) Australia Editorial Discussion ChatGPT draw on a vast set of data, synthesising information from multiple sources and can significantly impact nursing education and practice. Unlike traditional computer technology, AI has the capacity to think, learn, perceive, and make rational decisions without human intervention Using AI in assessment tasks raises concerns of academic integrity, plagiarism or even fraud if the AI system is not acknowledged as the source of information. Liu, et. al., (2023) China, USA Editorial ChatGPT, ChatGPT 3.5. ChatGPT has been found to be particularly useful in the field of education, as it can provide coherent and contextual answers to questions. Concerns over patient privacy, informed consent and maintaining professional boundaries. Maykut et.al. (2024) Canada Discussion ChatGPT, AI, OpenAI, general AI, chatbot. Students were introduced to the idea of integrating and analysing a ChatGPT response. The response was utilised as an initial brainstorming idea to expand the students’ knowledge of concepts. Resistance to AI integration stems from faculty unpreparedness, fear of the unknown, and ethical concerns, necessitating a balanced approach that exposes students to diverse technologies, teaches critical understanding of ethical implications, and guides integration through a humanistic framework centred on ethical and inclusive practices. Miao, et. al., (2023) USA Editorial Discussion ChatGPT, ChatGPT-4, Artificial Intelligence, OpenAI. Recent use of ChatGPT in various scientific questions, like intelligent transportation, drug discovery and nursing education, research, and practice Concerns over plagiarism. However, the ban on ChatGPT may not last long, as other companies, such as Microsoft, are integrating ChatGPT into their Office products. Ni et.al. (2024) China Discussion Artificial intelligence, ChatGPT, ChatGPT 3.5, ChatGPT 4.0, Large Language Models (LLMs), GPTZero, AI Text-Classifier, and ChatGPT Detector. ChatGPT software has been successfully used to pass the American Heart Association (AHA) Basic Life Support (BLS) test and the Plastic Surgery In-Training Exam (PSITE) ChatGPT’s reliance on offline data can lead to fabricated references, raising concerns among scientists about its impact on scientific transparency and ethical integrity in medical research. O'Connor (2021) Ireland Discussion Gen AI is hailed as a way to solve problems that impact health professionals, patients, students, and educators by improving the speed and accuracy of available information. There are concerns over productivity-oriented solutionism with virtual teacherbots, when higher education should be grounded in humanism. O’Connor (2023) UK Editorial Discussion OpenAI, ChatGPT 3.5. The Reflection (PAIR) framework, which outlines how to utilise gen AI tools, including the development and application of prompts. Educators and students may be concerned about fairness in accessing and using Gen AI tools, particularly when these tools are used to help students learn information and prepare assignments. O'Connor, et. al. (2023) UK and Canada Discussion ChatGPT is a powerful generative AI tool that uses algorithms to process large volumes of digital data, text, images, audio, and video and generate new content based on user input. The ethical use of AI-detection tools in education requires open faculty discussion, clear policies, and student transparency to address issues like false positives. Other concerns include threats to privacy, security, copyright, bias, and inaccuracy. O'Connor, et.al. (2024) UK, Canada, Hong Kong, Slovenia, US, Taiwan, Finland. Discussion A variety of GenAI tools, including image, auditory, text, video, large language models, and prompt engineering. ChatGPT can be used to help with unfolding case studies and role-playing exercises. There may be inaccurate or biased content which can be created by AI. Therefore, there should be a critical evaluation of AI-generated content. Pizzulo (2024) USA Discussion OpenAI, Artificial Intelligence (AI). ChatGPT helps to spark initial ideas. When teaching a topic without specific thoughts on engaging activities, it can generate a list of items. Factual errors due to ChatGPT's inability to comprehend a question, delayed updates on information, potential misuse, limited access to databases and biased responses. Quattrini et al., (2024) USA Discussion ChatGPT provides instant responses to prompts, supporting tasks like writing essays, generating exam questions and producing clinical documentation. Many educators are wary of AI’s ethical risks, particularly the potential for student misuse of ChatGPT. Therefore, leading to reluctance in embracing it as a learning tool. Reed (2023) USA Discussion Chat-based tools like ChatGPT and Bing Chat can be utilised for educational purposes. There are concerns, mostly due to cheating. With AI on the rise, nurse educators must find ethical ways to integrate it into their educational practices. Sharpnack (2024) USA Discussion AI technologies are vital for enhancing nursing education, serving as research assistants, simulating patient interactions, supporting communication, developing care plans, and enabling immersive problem-based and experiential learning to strengthen students' clinical skills, decision-making, and critical thinking. Data privacy and the need for comprehensive training to effectively interpret and employ AI-driven tools remain pivotal aspects to address when integrating AI into nursing education. Shay (2023) USA Discussion Advancements in AI highlight the need for nursing educators to understand their potential and limitations, as the technology has demonstrated capabilities in healthcare tasks, including passing exams and generating patient notes. Concerns exist around academic integrity and reliance on AI tools. Institutions recommend transparent use, disclosing the tool, sharing work processes, rather than generating complete assignments, focusing on established plagiarism detection methods. Silvestri-Elmore & Burton (2024) USA Discussion Applying AI, such as ChatGPT, to develop unfolding case studies in nursing education can promote active learning, enhance clinical judgment, and reduce barriers to implementing this strategy, thereby supporting clinical learning and critical thinking skills. Did not discuss ethical considerations. Simms, (2024) USA Discussion ChatGPT provides personalised, interactive learning experiences. The integration of AI is supported by constructivist theories and Vygotsky’s Zone of Proximal Development, which emphasise AI’s role in enhancing learners' development through tailored support and scaffolding. Gen AI tools challenge academic integrity, pose a challenge to validating information accuracy, and require strategies to ensure the credibility of AI-generated information. Simms (2025) USA Discussion Emphasises the importance of educators adapting curricula and teaching methods to effectively integrate generative AI learning, ensuring students are proficient in Gen AI technologies and aware of their ethical implications. Integrating AI in nursing education requires addressing ethical concerns around privacy, bias, transparency, and accountability through responsible use policies, human oversight, and critical evaluation, while using AI to supplement, not replace, traditional teaching and leveraging AI mistakes as learning opportunities to enhance students' critical thinking and responsible technology use. Srinivasan, et. al. (2024) India Discussion The use of chatbots like ChatGPT in nursing education offers benefits such as personalised learning, enhanced engagement, and increased efficiency, but also poses challenges and ethical concerns that need to be addressed Raises concerns about bias, privacy, security, accountability, and transparency, which can be addressed through comprehensive training data, strong privacy protections, stakeholder involvement, and ethical alignment to ensure a balanced, inclusive student preparation. Sun & Hoelscher (2023) USA Discussion ChatGPT, an artificial intelligence-driven, pretrained, deep learning language model, can generate natural language text in response to a given query. Its rapid growth has raised concerns about its ethical use in academia. Ethical challenges due to biases in training data can impact their accuracy. To address this, faculty should focus on designing assignments that promote self-reflection, critical thinking, and independent learning, while also teaching students to critically evaluate information and make informed decisions. Topaz et al. (2025) USA Discussion Gen AI helps nursing students by organising ideas, generating scenarios and realistic simulations, personalising tutoring and feedback, curating accessible learning materials, and automating grading to save instructors' time. Key risks include threats to academic integrity, bias, privacy, and copyright/plagiarism issues, as well as AI "hallucinations," which require fact-checking, ethical-use policies, and student education on the limits of AI. Thakur et al. (2023) Canada Discussion ChatGPT, Chatbot, OpenAI, mobile chatbot. Chatbots are an emerging AI application that simulates dialogue through audio or text and is capable of performing complex tasks, such as interacting with human users. Although not explicitly stated, the authors conclude that there have been concerns that ChatGPT may be misused. Zhou & Mui (2024) Malaysia Discussion Chatbot in nursing education offers a safe setting for nursing students to practice their skills. These chatbots simulate real-life clinical situations and interactions with patients. While AI can increase accessibility for diverse students, it raises ethical and privacy concerns, risks of overreliance that may weaken critical thinking, and has accuracy and depth limitations for complex or specialised nursing scenarios. Table 2: Empirical Research Examining Teaching, Learning and Assessment Applications of Gen AI in Nursing Education Author, Year of Publication Location Study Aim(s) Research Design Methodology Gen AI TLA applications Ethical Considerations Results Akutay, et.al., (2024) Turkey To evaluate the impact of AI on nursing students’ in-class case analysis lectures on their case management performance and satisfaction. Randomised Controlled Trial n= (188) Third-year nursing students: AI group (n=94 students), control group (n=94 students). Students' case management and diagnoses were assessed using various tools, and lecture satisfaction was measured using a Visual Analogue Scale (VAS). AI: DALL-E3 Image-Gen to create images based on researcher-written prompts. Did not outline any ethical considerations. AI group students scored higher in case management and knowledge tests, with no difference in satisfaction. AI-generated visual narratives can improve nursing education. Athilingam & He (2024) USA, Singapore This paper presents responses generated by ChatGPT in response to the authors’ prompt on nursing education, teaching, and learning. An exploratory, qualitative design n = (2) Nurse Educators: Given prompts for ChatGPT on nursing education, teaching, and learning. The authors provided evidence to support each response on the benefits, challenges, and controversies. Three prompts explored the background, methods, benefits, and ethical considerations of using ChatGPT in education and student assignments. Plagiarism, unfair advantage, bias, inaccuracies, data privacy and security issues, and a lack of accountability and responsibility. ChatGPT offers significant potential to enhance teaching and learning through personalised education, virtual tutoring, and automated feedback, but its use raises ethical concerns such as plagiarism, bias, and reduced critical thinking, necessitating clear policies and further research for responsible integration in nursing education. Basaran & Duru, (2024) Turkey To investigate the impact of Kahoot, ChatGPT, and traditional teaching methods on nursing students’ sexual health knowledge and attitudes. Quasi-Experimental Study n = (93) nursing students using an online survey. pre-test, a post-test, and a follow-up test. The Sexual Health Knowledge Test & The Sexual Health Attitude Scale for Youth measured students’ levels of sexual health knowledge and attitudes. ChatGPT as a teaching method for sexual health knowledge and attitudes [does not provide specifics of how this was achieved] Did not outline any ethical considerations All methods boosted confidence; ChatGPT had the strongest impact. Kahoot and ChatGPT improved knowledge more, while traditional methods better influenced attitudes. Overall, all methods had similar effects over time. Benfatah et.al., (2024) Morocco To investigate the impact of AI-assisted debriefing on the clinical development of nursing students compared to conventional debriefing techniques. A Comparative Study n= (40); nursing students; an experimental group receiving AI-assisted debriefing and a control group receiving debriefing without AI. The study used Deepgram for audio analysis of communication and OpenPose for video analysis of gestures, assessing clinical skills and interaction quality. A ChatGPT-3.5-based Chatbot system facilitated AI interactions during debriefing for the experimental group. Ensuring student privacy, transparency, addressing bias, establishing accountability, and creating guidelines for ethical AI use. AI-assisted simulation and debriefing improved students' confidence, engagement, and critical reflection more than traditional methods, with high gesture accuracy and better integrated asthma management, although clinical understanding remained similar across groups. Increased active participation and critical reflection were observed in the experimental group. Benfatah et.al., (2024) Morocco To identify subtle ways in which ChatGPT can address specific challenges and enhance the overall efficacy of training programs through its use in nursing education. Multifaceted Exploratory Study n= (12) nursing students: Examined acceptability, accessibility, engagement ratings, and assessed students' virtual patient interaction skills. For analysis, interactions were recorded. Participants in the study were assigned to the simulation individually and interacted with ChatGPT as if they were caring for a real patient to assess the usefulness of ChatGPT as a virtual patient for healthcare simulation. Did not outline any ethical considerations Students rated ChatGPT highly for accessibility, engagement, and usefulness. Overall performance in virtual patient interactions was good, with clarity and relevant answers being crucial for success. Bonacaro, et. al. (2024) Italy, Greece The study examines views of nurses, educators, and students on ChatGPT's potential impact on healthcare, education, and nurse-patient relationships. An observational study n= (176) participants, including nursing students (37.5%), nurses (32.4%), and educators (8%), who were voluntarily recruited from a university in Northern Italy. Data were collected through an online questionnaire. The use of AI (ChatGPT). AI may reduce direct human interaction, potentially weakening nurse-patient relationships and empathy, which could impact students’ ability to develop these skills. Most respondents (50-85%) view AI positively for improving nursing practice, education, and care, mainly in personalised training, cost savings, and inclusivity. They are sceptical about AI replacing staff or educators but see potential benefits in care quality, efficiency, diagnostics, and job satisfaction, especially in administrative tasks. Chang, et. al., (2022) Taiwan The research investigated whether a mobile chatbot enhances students’ learning and self-efficacy in an obstetric vaccination course compared to traditional teaching methods. A Quasi-Experiment [Pre- and post-testing] n= (36) nursing students: (n 18) experimental group using a chatbot-based learning approach and a control group (n=18) receiving traditional instruction, Pre and Post Test questionnaire and Interviews with (n 10) participants. The Chatbot Disease Manager offers educational content on infectious diseases and vaccines, provides real-time COVID-19 updates, and tracks global epidemic trends to inform users about necessary travel precautions. Did not outline any ethical considerations Most students felt the mobile chatbot improved understanding and clinical preparedness by simulating real health issues. It helped them apply knowledge and solve problems, fostering deeper learning. Adding features like image recognition could further enhance its usefulness. Chang, et. al., (2024) Taiwan Impact of a Gen AI-based art therapy approach compared to traditional art therapy instruction on students' empathy levels and art therapy performance. Quasi-experimental Design n = (65) undergraduate nursing students used a modified empathy questionnaire and art therapy rubrics to assess empathy levels and evaluate their art therapy works based on expression, emotional depth, colour use, symbolism, and creativity. Week 1 - introduction to gerontological nursing and pre-tests; Week 2 - experimental group using Gen AI-based art therapy with SDL phases, while the control group used traditional digital art therapy with similar SDL stages; Week 3 - post-tests for both groups. Did not outline any ethical considerations The experimental AI group showed significant improvements in empathy and art therapy skills, excelling in expression, colour use, symbolism, and creativity, though not in emotional depth. Analysis highlighted strong links between expression and colour, with room for growth in creativity Dorin & Atkinson (2024) USA Faculty's understanding of AI-Natural Language Platforms and their pedagogical applications for integration into coursework. Qualitative Exploratory Research n (98) Participants: n (51) evaluators, n (47 ) instructors completed a survey instrument that was developed to understand current nursing faculty perceptions and knowledge of AI-NLP platforms. Artificial intelligence-natural language processing (AI-NLP) platforms (ChatGPT OpenAI, AI-NLP classifier), their pedagogical uses and integration into coursework. Institutions need guidelines to address academic integrity, emphasising fairness, genuine learning, and human involvement in education. Top of Form Most nursing faculty are unfamiliar with AI-NLP platforms and their use, but many acknowledge the need to understand their capabilities, identify student use, and integrate the technology into coursework. Fenske & Otts (2024) USA Assess students' ability to analyse the strengths and weaknesses of a GenAI research tool, focusing on accuracy, relevance, and efficiency. A descriptive, observational study n = (323) graduate nursing students compared search results for their Phenomenon of Interest (POI) using PubMed, CINAHL, and Elicit (an AI tool). They analysed Elicit's result generation process and selected two relevant articles for their POI. Elicit is an artificial intelligence research assistant that utilises language models to automate part of researchers' workflows. Elicit utilises semantic similarity to identify articles sourced from the corpus of Semantic Scholar. Users must critically evaluate AI-generated content, verify accuracy, protect privacy, avoid over-reliance, and consider equitable access while using AI tools responsibly. CINAHL (31.6%), PubMed (30.7%), and Elicit (26%) were the top choices among users, with CINAHL favoured for nursing-specific content, PubMed for its extensive database, and Elicit for speed and ease of use despite technical limitations. 11.8% had no preference. Gonzalez-Garcia et al (2025) Spain Evaluate the impact of ChatGPT on nursing students’ education and determine how it influences their learning outcomes. Quantitative cross-sectional design n = (98) nursing students: AI knowledge, and perceptions of ChatGPT as an educational tool questionnaire. After completing the course, students evaluated ChatGPT’s effectiveness in enhancing their skills and knowledge. Eight nursing case studies, students used ChatGPT to generate solutions before they were presented. The cases simulated real-world challenges and provided guidance on using AI for problem-solving. Did not outline any ethical considerations ChatGPT use correlated with improved academic performance, with 89.5% of students reporting significant grade improvements. Women found it particularly helpful for academic tasks, and a positive link was observed between prior ChatGPT use and higher GPAs. Gotkas et.al. (2024) Turkey, Ireland To develop guidelines on how to employ ChatGPT 4.0 using the prompt learning method. Case Study In the method of prompt learning, users provide specific prompts, and the AI model responds according to its training, specifically aligning with the progression of knowledge and skills in nursing education as outlined by Benner's theory. Using ChatGPT 4.0 With Prompt Learning for Educational Purposes. How to employ ChatGPT 4.0 using the prompt learning method. Users must be aware of AI limitations, avoid sharing sensitive information, and craft unbiased prompts to ensure the equitable and responsible use of AI while protecting both privacy and accuracy. The customisation of a conversational AI chatbot shows support for the development of nursing knowledge and skills. This paper outlines how to integrate Benner's theory with ChatGPT's capabilities, addresses bias issues, and establishes best practices for the safe and effective use of AI in nursing. Han, et.al. (2022) Republic of Korea To develop and evaluate the effectiveness of an AI chatbot educational program for teaching nursing students electronic fetal monitoring skills Quasi-Experimental Study n = (61) nursing students; n = (31) control group, and n (30) experimental group examined the effect of an AI chatbot educational program on EFM skills, with the experimental group receiving both video and chatbot lectures, while the control group received only video lectures. An artificial intelligence chatbot educational program for promoting nursing skills related to electronic fetal monitoring. The transactional nature of the chatbot design limits its ability to provide personalised, specific feedback, which could restrict effective learning and accurate correction, raising concerns about the chatbot's reliability and impact on student understanding. The study found no significant differences between the experimental and control groups in terms of knowledge, clinical reasoning, confidence, feedback, or satisfaction. However, the experimental group showed significantly higher interest in education and self-directed learning. Hawk, et. al., (2024) USA Explore nursing students’ perspectives on using a chatbot to answer a clinical question. Qualitative Descriptive Study n = (19) nursing students. A qualitative approach, utilising reflective thematic analysis of written reflections, was employed. Students used a chatbot to answer a clinical question Requires human oversight and critical validation of AI outputs to ensure accuracy and mitigate risks such as fabricated sources Students recognised the chatbot's accuracy and familiarity, but stressed the need for critical validation, noting its helpful yet shallow summaries, and shifted from initial scepticism to cautious optimism about its ethical use in nursing. Higashitsuji et.al. (2025) Japan Clarify the effectiveness of ChatGPT in case-based learning by using this application for case creation, Single-group pre-post design and a blinded nonrandomized crossover design. n= (17); n=(8) faculty members and n=(9) students; evaluated faculty-created cases and ChatGPT-generated cases, with nursing students discussing the cases in recorded groups, analyzed through surveys and transcripts. Using ChatGPT to create case studies. Educators must still critically address concerns about data privacy, ethical issues, and the accuracy of generated content. Case creation time differed significantly, with 106 min and 71 min for manual and. ChatGPT, respectively (95% CI = 1.0–1,299.6, p = 0.042). There were no significant differences in the perceived burden of creation and discussion quality. Jhallad et.al., (2024) Palestine and Jordan To determine the factors that affect the usefulness and sustainability of artificial intelligence tools used in nursing education. Descriptive Cross-Sectional Study n= (204) undergraduate nursing students A Questionnaire utilising the Technological Acceptance Model (TAM), the Information System Success Model (ISSM), and the Online Learning Self-Efficacy (OLSE) was used. Outlines general applications of Gen AI in nursing education through virtual simulations, personalised learning resources, remote clinical practice, and immersive VR/AR environments that improve clinical skills, decision-making, and teamwork. Did not outline any ethical considerations Students' learning achievements were the top factor influencing AI tool use, with perceived enjoyment and intention to use also highly rated. Information quality and system quality were the most important factors, and mobile apps were the most widely used AI tools, followed by ChatGPT, PowerPoint AI, VR, and simulation. Kapadia, et.al. (2024) USA To evaluate the impact of LLM-generated dialogues on the training and development of nursing students. Comparative Case Study Using lexical and semantic similarity tests compared to clinical expert-generated scripts, assessed the potential of LLMs as suitable alternatives for script generation. The application of LLMs within VR environments to simulate patient-nurse dialogues. Understanding, adapting models to healthcare contexts, and the implications of AI use in patient care scenarios. GPT-3.5 excels in Health History scenarios with good alignment to human evaluation, but shows gaps in doctor portrayal during Bedside Conversation simulations, indicating a need for improvement Karaçay & Yaşar (2025) Turkey To reveal sophomore nursing students' experiences and perspectives toward ChatGPT activities used as a teaching tool in the classroom. Descriptive qualitative design n= (24) sophomore nursing students who participated in ChatGPT activities in the classroom responded to a researcher-developed survey. 2 hours of theory and 2 hours of lab on abdominal and neurological exams included a student activity where participants summarized the lectures using ChatGPT-3.5 Did not outline any ethical considerations ChatGPT classroom activities increased participants' knowledge, critical thinking, and satisfaction, facilitating group work and access to information, while highlighting the need to evaluate AI responses critically. Kong, et. al., (2024) China To understand nursing students' experience of using artificial intelligence for learning. Qualitative study n (14) undergraduate nursing students shared their experiences and feelings with AI during semi-structured interviews, offering suggestions for course content, with data analysed thematically. Chatbots built (ChatGPT, OpenAI) Over-reliance on AI, responsible use respecting laws and values, and educators' role in guiding responsible AI use and digital literacy Students felt a mix of confusion, excitement, and motivation with AI, developing confidence, teamwork, and benefiting from teacher support, while showing interest in further learning and improved teaching methods. Kowitlawakul, et.al. (2024) Singapore, Thailand, USA, & Ireland To evaluate the usability and perceptions of a newly developed AI-TAS (Teaching Assistant System) for a leadership and management module. Pilot Field Test n = (21); undergraduate nursing students, demographic data form and usability questionnaires, followed by participating in a focus group interview. Artificial Intelligence- Teaching Assistant System (AI-TAS): Conversations on topics like conflict management, change, time management, and problem-solving, using the chatbot to inquire about course content and promote interaction. Reliance on AI may lack depth and understanding, with technical issues and limited content affecting engagement, highlighting the need for human oversight Participants valued AI-TAS for revision and support, but suggested improvements in usability, features, engagement, and the integration of quizzes, gamification, and human tutoring to enhance effectiveness. Lane, et. al., (2024) USA The use of AI in nursing education presents a guide to Gen AI implementation in nursing education. Case-Based Descriptive Study n= (95) Nurse Educators completed a SWOT analysis on implementing Gen AI, analysed thematically using Kiger and Varpio’s (2020) method. ChatGPT assist with administrative and educational tasks like summarising research, creating syllabi, drafting letters, reviewing papers, developing exam questions, designing case studies, and crafting assessment rubrics Unclear guidelines, conflicts with nursing values, academic dishonesty, AI biases, and the need for responsible AI literacy and ethics in education AI tools like ChatGPT offer benefits such as time-saving, improved simulation, and promoting critical thinking, but also pose risks including inaccuracies, hindering creativity, ethical concerns, and potentially replacing faculty roles. Lebo & Brown (2024) USA To examine the application of AI patient case simulations in nursing education. An exploratory case study n=(50); undergraduate nursing students; simulation performance was objectively assessed with a computer-generated score based on assessment thoroughness, patient interviews, and completed questions during the simulation. AI patient cases Bias in AI simulations may occur if scenarios lack diversity, and inaccuracies in avatar responses or features could lead to misconceptions or inadequate practice. Students generally responded positively, valuing the simulation for practising dialogues and medication review, but some found asking complex questions challenging and wanted more assessment options due to AI limitations in simulating physical findings. Liaw, et. al. (2023) USA, Singapore To evaluate nursing students’ competencies and experiences in communicating with an AI medical doctor. A Mixed-Methods Design n= (32) undergraduate nursing students completed pre- and post-tests on communication skills and self-efficacy, used surveys to evaluate experiences with AI-enabled VRS, and 5 participated in focus groups for deeper insights. The development of a novel AI-enabled virtual reality simulation (AI-enabled VRS). Cheating, plagiarism, and AI hallucinations, but features like data control aim to mitigate privacy risks. Nursing students significantly improved in communication skills and self-efficacy after using the VRS, found the AI agent helpful, and viewed the simulation as acceptable and useful, recommending it as a complement to, not a replacement for, face-to-face training. Makhlouf et.al., (2024) Egypt and Saudi Arabia To evaluate the effectiveness of designing a knowledge-based artificial intelligence chatbot system for a nursing training program Quasi-experimental design n= (73) nurses: the study used multiple validated instruments to assess nurses’ demographics, AI knowledge, perceptions of AI in nursing care, opinions on AI use, and views on nursing chatbots. A six-month study developed and integrated a chatbot into training, evaluating its effectiveness through surveys on nurses' knowledge, perceptions, and skills across specialities. Did not outline any ethical considerations AI chatbot improved nurses' knowledge, perceptions, and attitudes, particularly in patient safety and care efficiency. Nurses viewed chatbots as valuable and user-friendly tools to support safe, evidence-based nursing and enhance patient outcomes. Miao & Ahn , (2024) China To assess the performance of ChatGPT-4 on multiple-choice and open-ended questions derived from nursing examinations in the Chinese context. An Exploratory Study The data sets of the Chinese National Nursing Licensure Examination spanning 2021 to 2023 were used to evaluate the accuracy of GPT-4 in multiple-choice questions. The performance of GPT-4 on open-ended questions was examined using 18 case-based questions. Evaluate GPT-4's performance on nursing exam questions in China, covering multiple-choice and open-ended items Clear guidelines are essential for the ethical use of technology by students, along with vigilance to prevent educational disparities and social biases. Chat GPT-4 achieved 71% accuracy on nursing questions over three years, excelling in calculations but limited in social, ethical, psychological, and image-based queries, with moderate performance in open-ended responses and challenges in specificity, prioritisation, and current standards. Molu (2025) Turkey Compare the effectiveness of an AI-based care plan learning strategy versus standard training on nursing students' newborn resuscitation outcomes. A Quasi-Experimental Study n=(72) nursing students; n= (36) experimental group, which received care plans based on AI, n= (36) control group, which received traditional instruction with pre- and post-tests assessing neonatal resuscitation knowledge and student information. Using Chat GPT to create an AI-based care plan learning strategy. ChatGPT lacks clinical expertise, real-time data access, and the latest guidelines, requiring careful use and further research in AI care plans. The AI-based care plan group showed significantly better learning outcomes in newborn resuscitation post-test, with students expressing positive views and recognising AI's benefits for nursing. Parker, et. al., (2023) USA Explores the suitability of ChatGPT for automated writing evaluation in writing instruction. Quasi-Experimental Design n = (42) undergraduate nursing students: n=(18); first year undergraduate nursing students, n= (24) third-year graduate nursing students: 42 texts were analysed. Each text was entered separately into ChatGPT-3 following an input prompt that included a rubric with constructs of language proficiency intended to represent aspects of complexity, accuracy, and fluency (CAF), commonly used as criteria for assessing writing development. Concerns include student cheating, plagiarism, and AI's tendency to generate inaccurate or unjustified information ChatGPT graded more strictly, offering detailed, human-like feedback focused on macro-level writing aspects and suggesting improvements, demonstrating its usefulness as an automated writing evaluation (AWE) tool. Reed & Dodson (2024) USA To determine how the use of Gen AI patient backstories as a pre-simulation strategy. Qualitative Cross-Sectional Survey n= (15) junior-level BSN students, using a researcher-developed survey. Content analysis was completed following the simulation. The use of AI to create patient stories for simulations. Concerns over unethical student use and plagiarism hinder the integration of technology. AI pre-simulation generation enhances simulation prep by reducing anxiety, improving knowledge, and strengthening emotional connection with patient stories. Reeder & Lee (2024) USA To understand what assignments need to be revised or replaced if Gen AI could produce automated answers sufficient to pass a given course assignment. Mixed Methods ChatGPT's ability to generate passing responses in two online graduate courses, in Nursing Informatics and Data Science, using a minimal user approach and evaluating its replies against course rubrics in 12 key discussions. To "proof" assignments against inappropriate use of Gen AI tools (ChatGPT). Course policies should be revised to acknowledge appropriate AI use, and individual meetings should assess students' understanding and application of AI tools ChatGPT failed to produce passing responses in Nursing Informatics discussions but could pass three of four assignments in Data Science, with students reporting AI use in professional practice for tasks like technical documentation and qualitative analysis. Saatçi, et.al., (2024) Turkey To examine the effect of nursing students’ use of artificial intelligence (AI) tools while preparing patient education materials on the understandability, actionability and quality of content material. Randomised Controlled Trial n= (180) nursing students were divided into a control group (89) using traditional resources and an intervention group (91) using AI tools plus traditional resources; their patient education materials were evaluated using PEMAT (Patient Education Materials Assessment Tool) and the Global Quality Scale. Using Gen AI tools to create patient education leaflets, ChatGPT, Copilot and Gemini. Did not outline any ethical considerations Students using AI tools like ChatGPT, Copilot, and Gemini showed significantly higher PEMAT scores in understandability, actionability, and quality, indicating AI integration enhances educational content effectiveness. Saban & Dubovi (2024) Israel Explores the potential of the Gen AI tool (ChatGPT) as clinical support for nurses and nursing students. Cross-Sectional Study n= (30) Emergency room registered nurses (i.e. experts); n= (38) nursing students. Evaluated clinical decision-making across scenarios, comparing responses and performance metrics such as time and length using questionnaires and ChatGPT responses. Top of Form Comparing ChatGPT responses to clinical scenarios to those of nurses on different levels of experience using the AI model, OpenAI, ChatGPT, large language models LLM, and OpenAI Interface. Ensure cautious and transparent use and responsibly integrate it into education and practice to safeguard patient safety, prevent bias, and maintain clinical judgment. ChatGPT's triage performance was comparable to that of experts and novices, but it tended to over-triage and suggest unnecessary tests. However, it provided faster response times and more detailed, uncertainty-indicating responses that sometimes included additional diagnostic information. Shin et.al., (2024) Korea To evaluate the effects of artificial Intelligence-assisted learning on nursing students' ethical decision-making and clinical reasoning. A Quasi-Experimental Study n=(99) nursing students: n= (52) experimental group, n = (47) control group. Using student reports, two evaluators independently graded the scores based on two criteria (ethical standards and nursing processes) for the two groups. Both groups reviewed a child abuse case involving a 4-year-old with health issues, with the experimental group using AI ChatGPT 3.5 and the control group using textbooks to explore ethical and professional considerations. Nursing curricula should include ethical guidance on AI, focusing on legal considerations, responsible data management The control group outperformed the experimental group in terms of ethical understanding and critical thinking, utilised a wider range of resources, and demonstrated greater confidence in their learning. In contrast, the experimental group relied more heavily on textbooks and expressed concerns about the reliability of AI. Simsek-Cetinkaya, et. al., (2023) Turkey To evaluate the effectiveness of AI-assisted screen-based simulation in teaching breast self-examination skills in nursing undergraduate students Comparative intervention trial n= (103) first-year nursing students: split into AI-assisted screen-based simulation (52) and standard patient simulation (51) groups; data were collected via student forms, breast examination checklist, satisfaction and confidence scales, and anxiety inventories. AI-powered virtual patient simulation Did not outline any ethical considerations The standard patient simulation group scored highest in breast self-examination skills, with significant differences between groups. The AI-assisted screen-based simulation group had higher anxiety and satisfaction levels, both with significant differences. Tseng et.al., (2025) Taiwan The effectiveness of AI literacy and the application of ChatGPT and Copilot in academic nursing report writing. Exploratory Case Study n= (203); Senior nursing students compared AI-enhanced teaching using ChatGPT and Copilot with traditional methods in a course on case report writing, using the ADDIE model and scaffolding, measuring AI literacy with MAILS, and evaluating performance through standards-based reports Chat GPT; Large language models such as ChatGPT, Copilot, and BERT Responsible AI use, accountability, supervision, and maintaining professional standards as key ethical considerations in nursing education and practice. High reliability of the MAILS questionnaire, with significant improvements in AI literacy, creation, self-efficacy, and self-competency after an 18-week AI intervention. Students extensively utilised AI tools like ChatGPT and Copilot, and the experimental group scored higher in assessments, demonstrating strong consistency and positive correlations between self-efficacy, literacy, and competency. Vaughn et al. (2024) USA To evaluate the effectiveness of using ChatGPT as a tool in simulation design. Post-test survey design n (18) reviewers, 3 Adult Health Nursing subject matter experts, 9 simulation design experts, and 6 sim-ops speciality experts reviewed the ChatGPT-generated simulation scenarios and completed the survey. The study describes how ChatGPT generated five simulation scenarios in less than 15 seconds each, and gave a title and listed scenario objectives for each one that were realistic based on the subject matter Careful review, inclusive language, and caution are needed, emphasising the importance of critical evaluation and expert input to avoid inaccuracies in educational content. Data analysis showed that scenarios differed in realism and completeness, with some missing pertinent details but accurate information overall; reviewers generally responded positively, often surprised by the richness of content in each scenario White, et. al., (2024) USA To determine if a three-phased educational intervention significantly improved nursing students’ attitudes toward older adults. A pre-/post-test study design n = (151) senior-level community health nursing course students participating in an AI in Education event. The study used the UCLA Geriatrics Attitudes Survey to assess changes in nursing students’ attitudes toward older adults. AI-driven, virtual, non-immersive simulation experience Did not outline any ethical considerations Results showed a slight increase in scores after the intervention (mean = 35.07 vs. 34.50), with a small effect size (d = 0.15). The improvement was marginally significant at the 0.10 level (t = 1.88, p = 0.06). Wolf (2023) USA Evaluating the impact of an Automated Essay Scoring assessment on MSc Nursing student writing skills. Quasi-Experimental design n= (64) MSc nursing students: Pre-and post-test survey data on student self-efficacy and task value, and automated essay scoring (AES) of writing proficiency were measured An AI-powered writing assessment, IntelliMetric®, the AI system was trained to recognise patterns in an effective persuasive essay and calculate scores based on the faculty’s scoring of hundreds of essays responding to the same prompt. Supporting diverse students, and using automated assessments, emphasising the efficiency, reliability, and validity of the data produced. The course significantly boosted students’ confidence and writing skills, improving various proficiency areas, while their perceived value of scholarly writing remained high and unchanged. Wu et.al., (2024) China Evaluates LLMs' ability to answer NCLEX-RN and NNLE questions in multiple languages, assessing their potential as multilingual nursing education tools Cross-sectional study The study tested ChatGPT 4.0, ChatGPT 3.5, and Bard on original and translated NCLEX-RN and NNLE questions, comparing their accuracy across languages and models. large language models (LLMs) ChatGPT, for preparing for NCLEX exams Linguistic bias, privacy, informed consent, fairness, accountability, and the need for clear regulations to ensure the responsible and equitable application of this technology. ChatGPT 4.0 outperformed ChatGPT 3.5 and Bard in answering NCLEX-RN and NNLE questions across languages, achieving higher accuracy in both English and Chinese, with better performance on original English questions. Top of Form Table 3: Educators and Students' Attitudes to Gen AI Author, Year of Publication Location Study Aim(s) Research Design Methodology Ethical Considerations Results Abdel-Moat et.al. (2024) Egypt This study aimed to assess nursing interns’ perception of artificial intelligence applications in nursing. Descriptive design n = (425) nurse interns. Self-developed AI technology questionnaire and a personal and work-related form were utilised during clinical shifts over a two-month period (August to September 2023). The loss of human touch affects patient satisfaction, data privacy and security issues, and the risk of algorithmic bias leading to unequal treatment. Overall, interns have low awareness (34.8%) of AI's role in nursing, with slightly more positive attitudes (34.9%) than concerns (34.5%). Many are uncertain or fearful (45.5%) about AI replacing their roles, while only 13.8% are optimistic about its potential, indicating a gap in AI education and integration in nursing training. Ahmed et.al., (2024) UAE, Egypt Investigate the experiences of nursing students in the UAE regarding the barriers and opportunities associated with the use of ChatGPT. Descriptive qualitative phenomenological design n = (27) nursing students' experiences with ChatGPT at the University of Sharjah, UAE, using qualitative interviews to uncover both barriers and opportunities in its use Concerns over ChatGPT output, limited capability and security concerns. Lack of up-to-date information, privacy-related issues (unauthorised data sharing, fraud, other forms of misuse). Students recognise ChatGPT's advantages, such as saving time, providing 24/7 access, and supporting personalised learning. However, concerns about privacy, cognitive impact, reliance, and information reliability influence their acceptance, highlighting the need for ethical guidelines and digital literacy education to ensure responsible use. Alenazi, (2025) Saudi Arabia Investigate factors influencing nursing students’ acceptance and use of AI. Cross-sectional study n = (213) nursing students examined how the Unified Theory of Acceptance and Use of Technology (UTAUT) factors, Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, affect their intention to use and actual use of AI. Briefly addresses privacy and data security concerns. Perceived usefulness strongly influences behavioural intention and actual use among nursing students, with behavioural intention mediating this relationship; effort expectancy, social influence, and facilitating conditions had less impact, and the model explained moderate to weak variance in intention and use. Bouriami, et. al., (2025) Morocco To examine nurse educators' knowledge, attitudes, and perceptions of ChatGPT, as well as their experience of using ChatGPT in active teaching methods. Cross-sectional pilot study n = (104) nurse educators. Used a self-developed questionnaire based on key concepts identified in a literature review. Risks of plagiarism, cheating, and the need for clear policies and training to promote responsible and ethical use among educators and students. Over half, 55.76% of respondents, saw ChatGPT as a tool that enables student cheating, while the majority, 70.52% were indifferent to its use. Additionally, 40.77% avoided using it and opposed student use due to concerns about inaccuracy and the potential for misinformation. El-Sayed et.al., (2024) Saudi Arabia, Egypt Explores how AI literacy influences the relationship between an innovation mindset and nursing students' confidence in their career and talent skills (CTSE). Cross-sectional study n = (596) nursing students. Correlation and regression analyses to explore the relationship between AI literacy and innovative thinking, as measured by the Innovative Thinking Competencies Scale (ITCS). The Scale for the Assessment of Nonexperts’ AI Literacy (SNAIL), The Career and Talent Development Self-Efficacy Scale (CTD-SES) Does not explicitly raise specific ethical concerns; it emphasises the importance of ethical awareness and responsible integration of AI in nursing education and practice. Nursing students had moderate levels of AI literacy, innovation mindset and career and talent self-efficacy. AI literacy significantly moderates the relationship between nursing students’ innovation mindset and their career and talent self-efficacy, making it more positive. El-Sayed et.al., (2025) Egypt and Saudi Arabia To examine the moderating effect of AI literacy on the associations between an innovation mindset and nursing students’ career and talent self-efficacy. Cross-sectional research design n = (596) nursing students; The Innovative Thinking Competencies Scale (ITCS) to assess nursing students’ competencies in innovative thinking. The Scale for the Assessment of Nonexperts’ AI Literacy (SNAIL) and The Career and Talent Development Self-Efficacy Scale (CTDSES) to assess nursing students’ perceptions of their CTSE. There is a vital need to teach responsible AI use and ethics in healthcare to help nurses navigate AI ethically while prioritising patient care, highlighting the importance of policies, guidelines, mentorship, reflective practices, and specialised AI ethics courses. Study revealed that nursing students had moderate levels of AI literacy, innovation mindset and career and talent self-efficacy. Nursing students’ career and talent self-efficacy were significantly predicted by their innovative mindset and AI literacy. AI literacy significantly moderates the relationship between nursing students’ innovation mindset and their career and talent self-efficacy, making it more positive. Kilci Erciyas et.al., (2024) Turkey Explore the connection between individual innovativeness levels and attitudes toward artificial intelligence among nursing and midwifery students. Cross-sectional, descriptive, and exploratory correlational design n = (500) nursing students: The Individual Innovativeness Scale (IIS), and the General Attitudes toward Artificial Intelligence Scale (GAAIS) Does not explicitly raise specific ethical considerations. Most students (94%) did not receive AI or innovation education, and over 97% did not participate in innovation research, resulting in generally low innovativeness levels, which were linked to more favourable attitudes toward AI. Students with AI training, innovative engagement, or ideas scored higher on innovativeness and positive perceptions, with the IIS significantly predicting attitudes—higher innovativeness correlated with more positive views of AI. Gunawan, et. al., (2024b) Thailand, Indonesia, Hong Kong, USA This study aimed to explore the perspectives of Indonesian nursing students on the utilisation of ChatGPT in their learning process. Qualitative Descriptive Design n = (29) second-year Indonesian nursing students. Focus Group Discussion (Deductive Thematic Analysis) Verifying AI-generated information, preventing plagiarism, and ethically integrating AI tools like ChatGPT into curricula highlights the need for nursing educators to teach responsible use, information validation, and maintain academic integrity. ChatGPT can be helpful but requires verification due to potential inaccuracies; core nursing skills like empathy and judgment cannot be replaced by AI, serving as tools for enhancement; and there is an urgent need to modernise curricula to incorporate AI, address ethical concerns, and better prepare students for technological advancements. Hashish & Alnajjar, (2024) Egypt and Saudia Arabia This study aimed to assess the perceived knowledge, attitudes, and skills of nursing students regarding digital transformation, as well as their digital health literacy (DHL) and attitudes toward AI. Furthermore, we investigated the potential correlations among these variables. Cross-sectional research design n = (266) nursing students a structured questionnaire consisting of six sections was used, covering personal information, knowledge, skills and attitudes toward digital transformation, digital skills, DHL, and attitudes toward AI. Descriptive statistics and Pearson correlation were employed for data analysis. Ethical considerations encompass data privacy, security, the responsible use of digital and AI tools, equitable access, and the need for guidelines to ensure the ethical deployment of technology in nursing education and practice. Students' acceptance of AI is influenced by their attitudes and varies with their level of digital device use. Issa et.al., (2024) Jordan, the UAE, the Kingdom of Saudi Arabia (KSA), and Egypt. The study examined students' knowledge of AI, training, attitudes toward integrating AI in health professions education (HPE), and their perceptions of barriers to implementation. Cross-sectional design n = (642) nursing students (n=218), medicine (n=173), nutrition (n=129), and physiotherapy (n=122). A self-developed, validated online questionnaire to assess students’ AI knowledge, attitudes, and perceived barriers in health professions education (HPE). Students should be guided on ethical considerations like patient privacy and confidentiality to build trust in AI in healthcare, with HPE programs emphasizing the development of ethical principles for handling big data to ensure responsible AI use. Participants showed limited prior AI knowledge, with 66.4% reporting low training levels, though familiarity with AI concepts varied across countries, especially higher in the UAE and Egypt. Most students accessed AI knowledge via social media and online resources. Overall attitudes toward AI integration in health education were positive, with over half (51.2%) holding high attitudes, particularly in Egypt and the UAE. Kang, et. al., (2023) Korea Identify nursing students' awareness of using chatbots and factors influencing their usage intention. Descriptive design using a self-reported questionnaire n = (289) nursing students self-reported questionnaires, both online via a Naver Form and offline. Adapted version of the Extended Technology Acceptance Model (ETAM). While not explicitly addressing ethics, the discussion implies considerations like chatbot reliability, professionalism, maintaining trust, and concerns about empathy and user trust in AI. Participants had moderate awareness and perceptions of chatbots' usefulness, ease of use, and intention to use, with awareness influenced by satisfaction, educational effectiveness, and interest. Perceived value was the strongest predictor, accounting for 60.2% of the intention to use. Labrague et.al., (2023) USA and the Philippines Aimed to examine student nurses' readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology. Cross-sectional study design n = (321) nursing students: Questionnaire perceptions of AI applications, barriers to AI access, and technological proficiency and understanding of AI. There may be barriers to access AI, such as a lack of computer skills, limited knowledge and awareness of AI, inadequate access to resources and time constraints. While over 79% are aware of AI in healthcare, only 40.6% are aware of AI in nursing, with a generally good understanding. Perceived barriers, such as a lack of knowledge, skills, and time, were moderate, and readiness to adopt AI was also moderate. Higher self-rated tech skills, better AI understanding, and positive perceptions predict greater readiness, accounting for 15.3% of the variance, with knowledge gaps and limited skills identified as key barriers. Liu, et al., (2024) China & USA To investigate the awareness and use of ChatGPT among second-year undergraduate nursing students Cross-sectional research design n = (46) undergraduate nursing students. A self-developed questionnaire examining awareness and use of ChatGPT. Did not include any ethical implications of implementing Gen AI as a teaching, learning, or assessment application. 97.8% (45 students) were aware of ChatGPT, with most having learned about it online. Among those aware, 23 students used ChatGPT, mostly infrequently (≤1 time per week), and one used it more frequently (2-3 times per week). Students primarily used ChatGPT to enhance their learning (16 students), complete homework (6 students), engage in conversation (5 students), and write essays (4 students). Luo et.al., (2023) China Aimed to investigate Chinese nursing students' AIQ and employability status, as well as their cognition and demand for the latest AI tool- ChatGPT. A cross-sectional survey n = (1788) students; AIQ–Self-Regulation–College Student Employability” questionnaire: Using correlation analysis and multiple hierarchical regression analysis, explored the relevant factors in the employability of nursing college students. Did not include any ethical implications of implementing Gen AI as a teaching, learning, or assessment application. Most students (81.3%) had not used ChatGPT, and 65.4% had never heard of it before, mainly due to a lack of access or knowledge. Despite limited usage, students generally had positive attitudes toward AI tools. Their moderate AI knowledge was linked to creativity, communication, learning, self-regulation, and better employability, with the lowest scores in data-related skills. Ma, et. al., (2025) China To explore the perceptions of nursing students and their experiences of using large language models and identify the facilitators and barriers by applying the Theory of Planned Behaviour. Qualitative descriptive design n = (24) nursing students (8 undergraduates, 11 postgraduates, 5 doctoral candidates) from 13 Chinese medical universities, using semi-structured online interviews in Mandarin and directed content analysis to explore factors influencing Large Language Model use among nursing students, following COREQ guidelines. Did not include any ethical implications of implementing Gen AI as a teaching, learning, or assessment application. Identified 10 themes based on the Theory of Planned Behaviour. Facilitators included perceived value, positive expectations, media influence, role models, and model design with free access. Barriers involved perceived caution, organisational pressure, geographic restrictions, and digital literacy gaps that hindered student adoption of AI. Migdadi et.al., (2024) Jordan, Saudi Arabia, Examine the correlation between AI ethical awareness, attitudes, anxiety, and intention to use AI technology among Jordanian nursing students. Descriptive, cross-sectional design n = (140) five-part questionnaire, covering (1) Sociodemographic data, (2) AI Ethical Awareness, (3) Attitudes Toward AI, (4) AI Anxiety, and (5) Intention-to-Use AI Technology {Test for AI Ethical Awareness (TAIEA)} Teaching and addressing ethical issues, such as privacy, fairness, and robot rights, are essential to promoting responsible development, use, and integration of AI in healthcare and education. Most student nurses have used AI and possess good knowledge; however, their ethical awareness, positive attitudes, and intent to adopt AI are low, despite experiencing low anxiety. Greater AI awareness is linked to more positive perceptions and willingness to use AI, emphasising the need for improved education to boost understanding and acceptance in nursing. Moskavich & Rozani, (2025) Israel To investigate the perceptions of health profession students regarding the use of ChatGPT and its potential impact on healthcare and education. Mixed-methods approach n = (217) of undergraduate health profession students with 73 (33.6%) nursing students, 65 (30.0%) medical students, and 79 (36.4%) occupational therapy, physiotherapy, and speech therapy students. The study employed a self-developed structured questionnaire. Students will outsource work to ChatGPT, and there is a need to promote responsible and ethical use of the platform in educational settings. Risks of academic dishonesty, privacy issues, the potential to undermine critical thinking skills, and the need for responsible and guided integration of ChatGPT. Most students (86.2%) were familiar with ChatGPT and viewed it positively, citing benefits like improved information access and innovative learning, though concerns about ethics, critical thinking, and verification were also raised. Perceptions were similar across health-related disciplines. Sumengen, et al., (2025) USA & Turkey To evaluate the attitudes of nursing students and literacy levels in relation to AI, to understand how prepared nursing students are with AI in clinical practice. A descriptive, correlational, and cross-sectional research design n = (366) undergraduate nursing students. The Artificial Intelligence Literacy Scale (AILS) and the General Attitudes Towards Artificial Intelligence Scale (GAAIS). Students are still confused about AI's ethical aspects, emphasising the lack of research on AI ethics in nursing and underscoring the need for comprehensive education on ethical issues associated with AI to ensure responsible and appropriate use in healthcare. AI literacy and positive attitudes toward AI increase with education, usage, academic level, and financial stability among nursing students, highlighting the importance of targeted AI education in nursing curricula to foster favourable perceptions and understanding. Yalcinkaya et al., (2024) Turkey To determine nursing students' attitudes towards and readiness for AI. Cross-sectional descriptive research design n= (291) at a nursing faculty in the west of Turkey, collected using the Individual Information Form, the General Attitudes towards Artificial Intelligence Scale (GAAIS), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) Does not explicitly outline any ethical concerns related to Gen AI in nursing education, but states that ethical considerations should be addressed in the curriculum. Students with positive attitudes toward AI, higher computer skills, and interest in AI applications tend to have greater readiness and more favourable perceptions of AI integration in nursing education. Subscale relationships showed positive associations between positive attitudes and cognition, ability, vision, and ethics. In contrast, negative attitudes had a weak negative correlation with cognition and a weak positive correlation with ethics. Yang, (2024) Korea Identify factors in artificial intelligence ethics awareness among nursing students. Descriptive Study n = (140) nursing students; A self-administered questionnaire, 18 items (Digital literacy), 36 items (Moral sensitivity), 24 items (AI ethics awareness). The focus on AI ethics awareness and education underscores the ethical need for responsible AI development and proper training of healthcare professionals to handle moral dilemmas and build trust. Most participants (75%) used AI devices, with high rates of ethics education (92.9%) and AI training (67.9%). AI ethics awareness was positively linked to digital literacy and moral sensitivity, with moral sensitivity being the primary factor, accounting for 14% of the variation and associated with greater AI ethics awareness among nursing students. Yigit & Acikgoz, (2024) Turkey To evaluate the knowledge, attitude and anxiety levels of future nurses about artificial intelligence applications Descriptive n = (552) nursing students; ‘Artificial Intelligence Anxiety Scale’ (AIAS). While not explicitly outlining ethical considerations, the discussion highlights concerns such as confidentiality, data security, and moral responsibilities. The average AI ethics awareness score was 51.68, with no significant link to demographics. Despite lacking formal training, students demonstrated a strong interest in AI education and viewed AI as beneficial for workload reduction and patient follow-up. However, they also expressed concerns about legal, ethical, privacy, empathy, employment, and security issues. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Jan, 2026 Read the published version in BMC Nursing → Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers invited by journal 13 Sep, 2025 Editor invited by journal 10 Sep, 2025 Editor assigned by journal 10 Sep, 2025 Submission checks completed at journal 10 Sep, 2025 First submitted to journal 04 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7537351","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517707536,"identity":"2d80fa2c-3063-4139-8c8c-8e3fcd7ba12d","order_by":0,"name":"Philip Hardie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACCSDmMWCQA1IGDAmkaDHmIVELA0NiD0gLUcBcuvnZhzcFNun72Q9vYHhQQYQWyznHjGfOMUjL7eFJK2BIOEOEFoMbCcbMPAaHc3skgD5KbCNKS/pnoJb/6TxgLf+I0pIDsuVAAkRLAxFaLGfkFDPOMUg27DmTVnAg4RgRWswl0jczvPljJ8/efnjjwx81xDgMmXOACA1ER98oGAWjYBSMaAAAym4xalYfQBMAAAAASUVORK5CYII=","orcid":"","institution":"Trinity College Dublin","correspondingAuthor":true,"prefix":"","firstName":"Philip","middleName":"","lastName":"Hardie","suffix":""},{"id":517707537,"identity":"3190787a-7892-4a0a-a2e2-95931608fd80","order_by":1,"name":"Andrew Darley","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Darley","suffix":""},{"id":517707540,"identity":"6428838d-cff2-4287-b538-3359b6aa96b7","order_by":2,"name":"Rosemarie Derwin","email":"","orcid":"","institution":"Trinity College 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14:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7537351/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7537351/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12912-025-04253-9","type":"published","date":"2026-01-16T16:30:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91953574,"identity":"dc7d9ebc-1b97-40c5-8db3-98b13c94329d","added_by":"auto","created_at":"2025-09-23 06:58:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179591,"visible":true,"origin":"","legend":"","description":"","filename":"MainBody.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537351/v1/2751b532d25ff08b4f2dfae2.docx"},{"id":91952134,"identity":"a90777eb-042b-44cd-9acf-9cb9b8bb726c","added_by":"auto","created_at":"2025-09-23 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1","display":"","copyAsset":false,"role":"figure","size":30899,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flow Diagram\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7537351/v1/308043b27ae2b9b68448f8ee.png"},{"id":100616105,"identity":"93c79d16-d52d-4f58-b55c-965304721db7","added_by":"auto","created_at":"2026-01-19 17:39:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2156646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7537351/v1/cfd01809-ab5b-4078-9b95-5bf3582ea976.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Applications, Attitudes and Ethical Considerations of Generative Artificial Intelligence (Gen AI) In Nursing Education: A Scoping Review.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGenerative Artificial Intelligence (Gen AI) is a rapidly advancing technology that utilises deep learning to produce human-like content in response to complex and varied prompts (Lim et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Gen AI generates content from Internet-based sources in response to user queries or searches. Gen AI is powered by machine learning and can perform diverse tasks with remarkable efficiency and improved accuracy as the technology continues to develop. These tasks include, but are not limited to, summarising or creating long or short-form content, image editing or designing, video and audio editing, transcribing, and checking lines of code, among other functionalities (Farrelly \u0026amp; Baker, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A growing number of Gen AI software tools are available for use, including ChatGPT, OpenAI, Claude, DeepSeek, Google AI, Microsoft Co-Pilot, among others, which are being increasingly incorporated into third-level education.\u003c/p\u003e\u003cp\u003eIn nursing education, Gen AI is shifting to learner-centred ecosystems that simulate real-world clinical complexity and facilitate personalised curriculum delivery to individual students' needs. Applications include AI-driven virtual patients and immersive scenario simulators that can adapt cases to a student's skill level (Martinez et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, it provides for the automated generation of realistic clinical notes and assessment questions (Shen et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), intelligent tutoring that offers targeted feedback and remediation (Wang et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and analytics that predict learners at risk and optimise curricula (Georgieva et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Gen AI also supports interprofessional teamwork exercises, real-time language translation for diverse cohorts, and streamlines faculty workload by summarising clinical performance and creating customised learning materials, thereby accelerating competency development while preserving patient safety (Alowais et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInvestigating the impact of GenAI is crucial for shaping the future of nursing education, ensuring the profession evolves in line with the rapid advancements in AI technology. While existing scoping and systematic reviews have successfully mapped and evaluated the literature to date on the topic ((Buchanan et al., (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Gerdes et al., (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Gunawan et al., (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Harmon et al., (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Hwang et al., (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Kovalainen et al., (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Labrague \u0026amp; Sabei, (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Lifshits \u0026amp; Rosenberg (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Luo et al., (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Ostick et al., (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Park et al., (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Ram\u0026iacute;rez‑Baraldes et al. (2025); Seo \u0026amp; Kim, (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)), the majority have focused on primary empirical studies or specific Gen AI applications, such as ChatGPT. This approach, while valuable, may overlook crucial information found in grey literature, including dissertations, conference papers, and pre-publication manuscripts. The rapid advancement of AI technology often outpaces the review process, potentially leading to outdated findings. Therefore, the omission of such sources may result in an incomplete picture of the rapidly evolving landscape. These limitations collectively underscore the need for a more comprehensive, rigorous, and up-to-date scoping review that incorporates a broader range of evidence sources to better capture the full scope and impact of AI in nursing education, especially given the field's emerging nature and potential for significant future developments. This review makes a novel contribution to the existing evidence synthesis by mapping and examining the range of available information on the attitudes of nurse educators and students towards Gen AI, based on their direct experience, together with the reported ethical considerations of its integration into nursing education.\u003c/p\u003e\n\u003ch3\u003eAim\u003c/h3\u003e\n\u003cp\u003eThis scoping review aimed to identify current trends and applications of Gen AI as a teaching, learning and assessment tool in nursing education. It also sought to establish potential ethical considerations associated with the use of Gen AI in nursing education and the measures taken to address these within the nursing education environment. By focusing on these critical areas, this review provides a comprehensive understanding of the current state of Gen AI in nursing education, while highlighting areas that require further research and directions for future curriculum development.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis scoping review follows the five-stage review process identified by Arksey and O'Malley (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which was later refined by Levac et al. (2010) and Peters et al. (2015). This methodological framework comprises the following stages: identifying the research question, identifying relevant studies, study selection, charting the data, and collating, summarising, and reporting the results, utilising the reporting scoping reviews\u0026mdash;PRISMA ScR extension outlined by Tricco et al. (\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) \u0026amp; McGowan et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The protocol for this scoping review was registered on the Open Science Framework (OSF) on February 20, 2025 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/9qe5m/\u003c/span\u003e\u003cspan address=\"https://osf.io/9qe5m/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Procedure\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStage 1: Identify the research questions\u003c/h2\u003e\u003cp\u003eFollowing an initial search of the evidence base, the current review was guided by the research question: \u003cem\u003e\"What does the literature reveal about the use and attitudes of Gen AI as a teaching, learning and assessment strategy in nursing education, including its associated ethical considerations?\".\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFour research objectives were central to answering this question:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo identify the extent and nature of the literature on applying Gen AI in nursing education as a teaching, learning, and assessment strategy.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo determine levels of student performance, satisfaction and confidence levels following the utilisation of Gen AI applications in nursing education.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo examine the attitudes of nursing students and educators to the application of gen AI as a teaching, learning, and assessment strategy in nursing education.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo establish potential ethical considerations associated with the use of Gen AI in nursing education.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStage 2: Identifying Relevant Studies\u003c/h3\u003e\n\u003cp\u003eThe authors included five healthcare and education-focused databases to search for articles, namely CINAHL Nursing and Allied Health (CINAHL Ultimate), ERIC (Proquest), Medline (EBSCO), Web of Science (Clarivate), and Applied Social Science Index \u0026amp; Abstracts. Peters et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) state that a clear scoping review question should incorporate elements of the PCC mnemonic (population, concept, and context). In this instance, the relevant population refers to nursing students, the concept is the application of Gen AI, and the context is the nursing educational environment.\u003c/p\u003e\u003cp\u003eThe three-step process recommended by the Joanna Briggs Institute was followed, starting with an initial search in EMBASE to derive key terms related to nursing students, Gen AI and nursing education. The search was refined with the support of a Nursing subject expert and a university librarian, and expanded across multiple databases, including grey literature sources. The strategy incorporated Boolean operators, truncation markers, and controlled vocabulary to ensure a thorough capture of relevant literature (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The grey literature search was conducted across six different platforms: OpenGrey (focusing on Humanities and Computer Science subjects), Google Scholar, Google, IEEE Xplore, Overton, ProQuest Dissertations \u0026amp; Theses. The search terms \"Generative Artificial Intelligence and Nursing Education\" were used consistently. The review included English-language publications from January 1st, 2014, to July 1st, 2025, focusing on Gen AI in nursing education. It included various research methodologies and literature types, while excluding non-English language publications and those examining Gen AI in non-nursing fields.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStage 3: Study Selection\u003c/h2\u003e\u003cp\u003eEach search was systematically documented, detailing the date, search terms, and results for each search string, as reported by two independent authors. The results were exported to Mendeley (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.mendeley.com\u003c/span\u003e\u003cspan address=\"http://www.mendeley.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for duplicate removal (n\u0026thinsp;=\u0026thinsp;419 duplicates removed) and then to Covidence (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.covidence.org\u003c/span\u003e\u003cspan address=\"http://www.covidence.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for screening. The initial search identified n = (1251) articles from the database searches and n = (142) articles from the grey literature search. A pilot test with 50 articles ensured consistent application of inclusion/exclusion criteria amongst the author team. Two reviewers independently screened n = (832) title and abstract and n = (188) full-text records, and conflicts were resolved by a third reviewer. A snowballing approach was also applied to cross-check the selected papers for inclusion against the reference lists of the identified review papers, resulting in the addition of four papers. A total of n = (89) papers were excluded at the full-text stage; n = (37) did not focus on nursing education, n= (13) focused on AI in healthcare, n = (19) were not available in English, n = (7) full-text papers were not available, and n = (13) were previous evidence synthesis papers. A total of n= (103) records were identified for inclusion. A PRISMA flow diagram was used to illustrate the screening process [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStage 4: Charting the data\u003c/h3\u003e\n\u003cp\u003eUsing the Joanna Briggs' Institute (2020) data charting documentation, data were extracted from the final set of included records in accordance with the objectives of the review and the research question. Consistent with scoping review methodology, these studies were not appraised for quality.\u003c/p\u003e\n\u003ch3\u003eStage 5: Collating, summarising and reporting the results\u003c/h3\u003e\n\u003cp\u003eThe extracted data were collated, critically reviewed, summarised descriptively, and prepared into a narrative synthesis by three of the authors.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStage 6: Consultation Exercise\u003c/h2\u003e\u003cp\u003eAs part of the consultation, the results of the narrative synthesis were discussed at a team meeting which was held to review the findings and their application to the review aim. The research team collaboratively derived the conclusions and presented them using tables for clarity and precision.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of selected studies\u003c/h2\u003e\n \u003cp\u003eFrom the n = (103) articles identified, an international representation of the literature is evident, with some studies involving multiple countries (Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). N = (116) countries included including the United States ( n\u0026thinsp;=\u0026thinsp;40) Australia ( n\u0026thinsp;=\u0026thinsp;2), China ( n\u0026thinsp;=\u0026thinsp;8), Egypt ( n\u0026thinsp;=\u0026thinsp;6), Finland ( n\u0026thinsp;=\u0026thinsp;1), Greece ( n\u0026thinsp;=\u0026thinsp;1), Hong Kong ( n\u0026thinsp;=\u0026thinsp;2), India ( n\u0026thinsp;=\u0026thinsp;1), Indonesia ( n\u0026thinsp;=\u0026thinsp;1), Iran ( n\u0026thinsp;=\u0026thinsp;3), Ireland ( n\u0026thinsp;=\u0026thinsp;3), Israel ( n\u0026thinsp;=\u0026thinsp;2), Italy ( n\u0026thinsp;=\u0026thinsp;2), Japan ( n\u0026thinsp;=\u0026thinsp;1), Jordan ( n\u0026thinsp;=\u0026thinsp;2), Korea ( n\u0026thinsp;=\u0026thinsp;4), Malaysia ( n\u0026thinsp;=\u0026thinsp;1), Morocco ( n\u0026thinsp;=\u0026thinsp;3), Philippines ( n\u0026thinsp;=\u0026thinsp;1), Singapore ( n\u0026thinsp;=\u0026thinsp;3), Slovenia ( n\u0026thinsp;=\u0026thinsp;1), Spain ( n\u0026thinsp;=\u0026thinsp;1), Taiwan ( n\u0026thinsp;=\u0026thinsp;4), Thailand ( n\u0026thinsp;=\u0026thinsp;1), Turkey ( n\u0026thinsp;=\u0026thinsp;10), UAE ( n\u0026thinsp;=\u0026thinsp;2), Saudi Arabia ( n\u0026thinsp;=\u0026thinsp;6), United Kingdom ( n\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eOf these, n = (59) empirical research articles ((Akutay, et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Athilingam \u0026amp; He (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Basaran \u0026amp; Duru, (2024), Benfatah et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Bonacaro, et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Chang, et. al., (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), Chang, et. al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Dorin \u0026amp; Atkinson (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Fenske \u0026amp; Otts (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Gonzalez-Garcia et al (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), G\u0026ouml;ktaş et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Han, et.al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), Hawk, et. al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Higashitsuji et.al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Jhallad et.al., (2024), Kapadia, et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Kara\u0026ccedil;ay \u0026amp; Yaşar (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Kong, et. al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Kowitlawakul, et.al. (2024), Lane, et. al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lebo \u0026amp; Brown (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Liaw, et. al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Makhlouf et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Miao \u0026amp; Ahn, (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Molu (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Parker, et. al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Reed \u0026amp; Dodson (2024), Reeder \u0026amp; Lee (2024), Saat\u0026ccedil;i, et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Saban \u0026amp; Dubovi (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Shin et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Simsek-Cetinkaya, et. al., (2023), Tseng et.al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Vaughn et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), White, et. al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Wolf (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e)); and n = (44) discussion/opinion/conference papers ((Alharbi (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Archibald \u0026amp; Clark (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Bumbach (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Byrne (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Castonguay, et. al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Chan, et. al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Chen (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Dante, et. al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), DeGagne (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) Degagne et.al. (2024), Lim (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Foronda \u0026amp; Porter (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Gapp et.al. (2025), Gehring, et. al. (2024), Ghane, et., al, (2024), Gonzalez (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Gosak, et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Harrison (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Irwin et. al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Jung (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Kelarijani et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lagadec, et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Liu, et. al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Maykut et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Miao et al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Ni et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), O\u0026apos;Connor (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), O\u0026rsquo;Connor (2023), O\u0026apos;Connor, et. al. (2023), O\u0026apos;Connor, et.al. (2024), Pizzulo (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Quattrini et al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Reed (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Sharpnack (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e, Shay (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Silvestri-Elmore \u0026amp; Burton (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Simms, (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Simms (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Srinivasan, et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Sun \u0026amp; Hoelscher (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Topaz et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Thakur et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e)) that explored various applications of Gen AI as a teaching, learning or assessment approach in nursing education. The empirical studies encompass a range of research designs, including quantitative studies such as randomised controlled trials, quasi-experimental studies, and cross-sectional surveys. The qualitative designs included exploratory, phenomenological, descriptive, and mixed-methods studies. A detailed description of the studies is presented in Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA narrative account of the review findings regarding the four study objectives is presented below.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eObjective 1: Teaching, Learning \u0026amp; Assessment Gen AI Applications in Nursing Education\u003c/h2\u003e\n \u003cp\u003eSeveral key themes emerged regarding the integration of Gen AI as a teaching, learning, or assessment strategy in nursing education. These included content creation and enhancement, simulation, personalised learning \u0026amp; tutoring and assessment.\u003c/p\u003e\n \u003cp\u003eIn terms of content creation, AI tools like DALL-E3 are being utilised to generate images for educational materials (Akutay et.al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), using ChatGPT AI image generation as a strategy to prepare for simulations (Reed \u0026amp; Dodson, 2024) or ChatGPT to create Gen AI art-based therapy materials (Chang et. al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), using ChatGPT to summarise lecture slides (Kara\u0026ccedil;ay, \u0026amp; Yaşar,2025), using ChatGPT to summarise research papers (Lane et. al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), using ChatGPT to create an AI-based care plan learning strategy (Molu, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), using ChatGPT, Copilot and Gemini to create patient education leaflets (Saat\u0026ccedil;i, et.al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), using ChatGPT to generate NCLEX-style questions (Miao et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSimulations and case studies represent a significant emerging theme in the application of Gen AI within nursing education, encompassing both their creation and implementation. Large language models such as ChatGPT and Gemini are increasingly utilised to develop realistic case studies (Higashitsuji et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) and to create interactive patient-nurse dialogue simulations (Kapadia et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, AI-powered patient interactions and AI-enabled virtual reality simulations (VRS) have been reported to enhance experiential learning (Jhallad et al., 2024; Simsek-Cetinkaya et al., 2023; Liaw et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lebo \u0026amp; Brown, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vaughn et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; White et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Students are actively encouraged to engage with AI tools, such as ChatGPT, to analyse case studies and explore ethical considerations, further supporting critical thinking and clinical reasoning skills (G\u0026ouml;ktaş et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e: Gonzalez-Garcia et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shin et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). ChatGPT has also been employed to facilitate individualised debrief sessions for students following a simulation (Benfatah et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003ePersonalised learning has been enhanced using AI Teaching Assistant Systems (AI-TAS) and chatbots that provide tailored support and address clinical questions, thereby promoting individualised learning experiences (Kowitlawakul et al., 2024; Makhlouf et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Han et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, conversational chatbots are increasingly employed to assist in teaching nursing skills, offering interactive and real-time guidance to students (Han et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gotkas et al., 2024; Hawk et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn terms of research, Fenske \u0026amp; Otts (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) outlined the use of Gen AI in conducting literature reviews, utilising the Gen AI research tool, Elicit. Students found that while Elicit was favoured by 26% for its speed and user-friendliness, it had limitations in accuracy and filtering capabilities, with CINAHL and PubMed preferred by 31.6% and 30.7% of students, respectively, for their specific strengths in nursing research (Fenske \u0026amp; Otts, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSeveral notable applications of AI in assessment have been identified, including AI-powered writing evaluation systems such as IntelliMetric\u0026reg; (Wolf, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This technology has also been utilised to support exam preparation, particularly for the NCLEX, by leveraging large language models (Wu et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, ChatGPT has been employed in Automatic Writing Evaluation (AWE), demonstrating its potential to provide formative feedback and enhance students\u0026apos; writing skills (Parker et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe discussion papers largely reinforce Gen AI\u0026apos;s potential to enhance learning experiences through personalised tutoring, realistic simulations, and efficient content generation. For instance, Alharbi (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Archibald \u0026amp; Clark (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasise how Gen AI can create tailored scenarios and save educators time by generating essays, exam questions, and automated feedback. Chen (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and DeGagne (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) discuss the role of Gen AI in enhancing clinical decision-making and reducing errors by providing instant access to tailored information based on individual students\u0026apos; needs.\u003c/p\u003e\n \u003cp\u003eSimilarly, Sharpnack (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Zhou \u0026amp; Mui (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) described how ChatGPT can summarise extensive research, simulate real-life clinical scenarios, support problem-based learning, and enhance student engagement. Gen AI\u0026apos;s ability to generate human-like responses and provide instant feedback makes it a valuable resource for both nursing students and their educators (Gehring et al., 2024; Quattrini et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Gen AI can also support curriculum development and offer immersive learning experiences through virtual reality applications (Topaz et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dante et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eObjective 2: Student Performance, Satisfaction and Confidence Levels Following the Use of Gen AI Applications in Nursing Education.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAkutay et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that students using DALL-E3-generated visual narratives achieved notably higher scores in case management (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and on knowledge tests for total hip arthroplasty (t\u0026thinsp;=\u0026thinsp;2.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), reflecting improved clinical performance. This study (Akutay et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and one other study (Saat\u0026ccedil;i et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) were randomised controlled trials. However, studies undertaken using other designs highlighted notable performance enhancements resulting from the integration of Gen AI in nursing education. Similarly, Gonzalez-Garcia et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) reported that students who employed ChatGPT to solve real-world nursing cases experienced substantial academic improvements, with 89.5% noting enhanced performance and a positive correlation between prior ChatGPT use and their GPA. Molu (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrated that a Gen AI-based care plan learning strategy using ChatGPT yielded higher post-test scores in newborn resuscitation compared to conventional methods, indicating positive learning performance following exposure to Gen AI intervention. Wu et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrated that ChatGPT-4 achieved an accuracy rate of 88.67% on NCLEX-RN questions, outperforming previous models and demonstrating effective performance across multiple languages, thereby highlighting its potential as a reliable clinical assessment tool. Benfatah et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) observed improvements in critical reflection, engagement, and clinical judgment in students participating in Gen AI-assisted debriefing and virtual simulations. Kapadia et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) further supported the efficacy of AI by demonstrating that LLM-generated dialogues within virtual reality environments effectively simulated patient-nurse interactions, with GPT-3.5 outperforming in health history scenarios and supporting realistic skill development. Rao et al. (2024) also noted that Gen AI-driven automated essay scoring led to significant gains in students\u0026apos; writing proficiency across multiple domains. Tseng et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) in Taiwan found that an 18-week AI literacy program led to marked improvements in students\u0026apos; report-writing abilities, as evidenced by higher assessment scores and robust evaluation metrics. Finally, Wolf (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) noted significant improvements in student confidence and writing skills following the use of an AI-powered writing assessment tool.\u003c/p\u003e\n \u003cp\u003eThe empirical studies indicate a mixed but generally favourable impact of Gen AI interventions on nursing students\u0026apos; satisfaction and confidence across differing contexts and applications. Akutay et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that using DALL‑E3 for image generation demonstrated significantly higher case management performance in the AI group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but no significant difference in satisfaction (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that performance gains do not necessarily translate into greater reported satisfaction. Similarly, Başaran and Duru (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that students taught with ChatGPT reported the highest confidence in managing sexual health issues (83.9%), yet satisfaction did not differ significantly from Kahoot or traditional methods. In contrast, Benfatah et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported high student ratings for ChatGPT\u0026apos;s accessibility (4.2), engagement (4.4) and usefulness (4.1), signalling strong satisfaction with ChatGPT as a virtual patient simulation tool despite some need for additional support.\u003c/p\u003e\n \u003cp\u003eKara\u0026ccedil;ay et al. (2024) observed increased knowledge, critical thinking and satisfaction linked to ChatGPT classroom activities. Simsek‑Cetinkaya et al. (2023) reported higher satisfaction for AI‑assisted screen‑based simulation versus standard patient simulation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); and Reed \u0026amp; Dodson (2024) found reduced anxiety, enhanced preparatory knowledge and stronger emotional engagement when AI‑generated patient backstories were used as a pre‑simulation strategy. Conversely, Lebo and Brown (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lane et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Başaran \u0026amp; Duru (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Moskovich \u0026amp; Rozani (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlighted students\u0026apos; positive perceptions, citing benefits such as accessibility, engagement, and usefulness. However, concerns about technical issues, limited engagement, and the need for human oversight were also reported. In terms of confidence specifically, Akutay et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Tural \u0026amp; Doymaz (2024), and Lebo \u0026amp; Brown (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that students using Gen AI tools, including ChatGPT and AI‑based simulations, reported increased confidence in clinical or educational skills, with Lebo \u0026amp; Brown (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) noting improved familiarity with patient interactions and Tural \u0026amp; Doymaz (2024) identifying enhanced self‑efficacy following Gen AI‑focused interventions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eObjective 3: Educator and Student Attitudes towards Gen AI in Nursing Education\u003c/h2\u003e\n \u003cp\u003eSeveral studies specifically examined students\u0026apos; and educators\u0026apos; attitudes, based on their direct engagement, toward Gen AI in nursing education (Abdel-Moaty et.al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Ahmed et.al. (2024), Alenazi (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Bouriami, et. al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), El-Sayed et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), El-Sayed et.al., (2025), Kilci Erciyas et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Gunawan, et. al., (\u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e), Hashish \u0026amp; Alnajjar, (2024), Issa et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Kang, et. al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Labrague et.al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Liu, et al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e, Luo et.al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Ma, et. al., (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Migdadi et.al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Moskavich \u0026amp; Rozani, (2025), Sumengen, et al., (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Yalcinkaya et al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Yang, (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Yigit \u0026amp; Acikgoz (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These studies have primarily utilised cross-sectional designs with structured questionnaires to investigate factors including AI literacy, acceptance, and ethical awareness. Among the tools used were the Artificial Intelligence Anxiety Scale (AIAS) (Yigit \u0026amp; Acikgoz, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), the General Attitudes Toward Artificial Intelligence Scale (GAAIS), the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) (Yalcinkaya et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the Artificial Intelligence Literacy Scale (AILS). Other relevant instruments include the Test for AI Ethical Awareness (TAIEA) (Migdadi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), the AIQ\u0026ndash;Self-Regulation\u0026ndash;College Student Employability questionnaire (Luo et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), the Extended Technology Acceptance Model (ETAM) (Kang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the Individual Innovativeness Scale (IIS). The GAAIS has also been referenced in studies by Kilci Erciyas et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), along with the Innovative Thinking Competencies Scale (ITCS), the Scale for the Assessment of Nonexperts\u0026rsquo; AI Literacy (SNAIL), and the Career and Talent Development Self-Efficacy Scale (CTDSES) (El-Sayed et al., 2025).\u003c/p\u003e\n \u003cp\u003eAdditionally, the Unified Theory of Acceptance and Use of Technology (UTAUT) (Alenazi, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) has been employed. Several researchers, including Abdel-Moat et al. (2024), Bouriami et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Hashish \u0026amp; Alnajjar (2024), Issa et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Labrague et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Liu et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Moskavich \u0026amp; Rozani (2025), developed their own questionnaires based on key themes identified from the literature. Qualitative methods, such as interviews and focus groups, were utilised to gain deeper insights into students\u0026rsquo; experiences and perceptions of Gen AI tools (Ahmed et al., 2024; Gunawan et al., \u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Ma et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). See Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for full details.\u003c/p\u003e\n \u003cp\u003eOverall, there is a generally positive outlook towards the use of Gen AI tools, such as ChatGPT and other large language models (LLMs), in enhancing nursing education. Alenazi (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) and El-Sayed et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight that nursing students perceive Gen AI as a valuable tool that can improve learning outcomes and academic performance. Alenazi (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) identified that the perceived usefulness and ease of use significantly influence students\u0026rsquo; willingness to adopt Gen AI. Similarly, Hashish \u0026amp; Alnajjar (2024), Sumengen et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) note that increased AI literacy correlates with greater acceptance and readiness to use Gen AI. However, Bouriami et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that while nurse educators generally accept ChatGPT, concerns remain regarding plagiarism, with educators who do not view it as a risk being more likely to use the tool.\u003c/p\u003e\n \u003cp\u003eOther concerns outlined by educators were the quality and robustness of outputs from ChatGPT. For example, Reeder \u0026amp; Lee (2024) reported ChatGPT failed to produce responses that would constitute a pass grade when tested in written assessment questions in a nursing informatics programme; however, it could pass three of four assignments from a data science programme. Moreover, Gunawan et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) report that while Indonesian nursing students see ChatGPT as helpful, they also view it as untrustworthy and emphasise the need for verification of Gen AI-generated information.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eObjective 4: Ethical Considerations of Gen AI Implementation in Nursing Education\u003c/h2\u003e\n \u003cp\u003eSeveral ethical concerns regarding the application of Gen AI in nursing education were evident in the literature. These concerns include issues related to privacy and data security, transparency, bias, academic integrity, human oversight and accountability.\u003c/p\u003e\n \u003cp\u003eThe discussion of ethical concerns was at the forefront of the vast majority of discursive papers ((Archibald \u0026amp; Clark (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Bumbach (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Byrne (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Castonguay, et. al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Chan, et. al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Chen (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), DeGagne (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) Degagne et.al. (2024), Lim (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Foronda \u0026amp; Porter (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Gapp et.al. (2025), Gehring, et. al. (2024), Ghane, et., al, (2024), Gonzalez (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Gosak, et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Harrison (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Irwin et. al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Jung (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Kelarijani et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lagadec, et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Liu, et. al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Maykut et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Miao, et. al., (2023), Ni et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), O\u0026apos;Connor (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), O\u0026rsquo;Connor (2023), O\u0026apos;Connor, et. al. (2023), O\u0026apos;Connor, et. al. (2024), Pizzulo (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Quattrini et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Reed (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Sharpnack (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e, Shay (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Simms, (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Simms (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Srinivasan, et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Sun, et. al., (2023), Topaz et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Thakur et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e)) indicating it is a primary concern for the implementation of Gen AI in Nursing education, with all but (Alharbi (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); Dante, et. al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); Silvestri-Elmore \u0026amp; Burton (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e)) outlining any ethical concerns.\u003c/p\u003e\n \u003cp\u003eBenfatah et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasise the importance of safeguarding student privacy and ensuring transparency in data collection and usage. Similarly, Gotkas et al. (2024) emphasise the importance of avoiding the use of confidential student or patient data when prompting Gen AI. This consideration is vital for protecting confidentiality and preventing discrimination or bias in the generated data, which is crucial for maintaining trust and fairness in Gen AI-driven educational tools. Benfatah et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Gonzalez (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Srinivasan et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) note that Gen AI systems can introduce biases stemming from incomplete or skewed data, which can lead to educational inequities. \u0026quot;Hallucinations\u0026quot; result in false or misleading information, raising concerns about the use of Gen AI in terms of academic integrity (Foronda \u0026amp; Porter, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liaw et. al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Simms, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Developing students\u0026apos; awareness of Gen AI\u0026apos;s limitations is crucial to ensure the responsible and effective integration of this technology (Topaz et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAnother significant ethical issue is the potential impact on interpersonal relationships, such as those between nurses and patients or students and educators. Bonacaro et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) caution that overreliance on Gen AI could diminish direct human interaction, potentially weakening the empathy and interpersonal skills essential for nursing practice. Abdulai et al. (2023) also note that ChatGPT\u0026apos;s inability to ensure confidentiality and its reductionist approach could undermine the holistic and empathy-driven care model that is central to nursing. Ghane et al. (2024) and Sharpnack (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasise the importance of educational training for nurses, educators, and students on the ethical use of AI tools, advocating for robust protections to safeguard sensitive information and maintain confidentiality.\u003c/p\u003e\n \u003cp\u003eResearchers (Archibald \u0026amp; Clark, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Athilingam et al., 2024; Dorin \u0026amp; Atkinson, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; and Lagadec et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) discuss the risks of academic dishonesty, including plagiarism and fraud associated with Gen AI-generated content, which could undermine the integrity of nursing education. Furthermore, Hawk et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Simms (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), and Topaz et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasise the importance of human oversight and critical evaluation of Gen AI-generated information to prevent inaccuracies, ensuring that Gen AI supplements rather than replaces traditional teaching methods. This is supported by Chen (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), who cautions against excessive reliance on Gen AI, as it may hinder students\u0026apos; critical thinking and problem-solving abilities, leading to a decline in independent thought.\u003c/p\u003e\n \u003cp\u003eIn terms of assessment, several discussion pieces discussed the potential misuse of Gen AI in particular for assessment methods that rely on text based responses ((Byrne (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), Castonguay et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Irwin et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Lagadec et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Miao et al. (2023), Ni et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), O\u0026apos;Connor (2023), Quattrini et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Reed (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Shay (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Topaz et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), and Thakur et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e)), highlighting the need to reframe assessment strategies to from traditional text-based methods to more hands-on, practical evaluations that protect against the potential misuse of gen AI by focusing on competency based clinical skills, performance-based tasks, problem based learning, that require critical thinking and personal judgment and the increased incorporation of oral assessments, including oral presentations, debates, and mini-viva, was also discussed.\u003c/p\u003e\n \u003cp\u003eResearchers (Archibald \u0026amp; Clark (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Benfatah et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lane et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lagadec et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Simms (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e)) emphasise the importance of developing comprehensive institutional policies that guide the ethical application of Gen AI tools, while advocating for staff training to mitigate potential issues related to academic integrity. O\u0026apos;Connor et al. (2023) further emphasise the need for transparent policies that foster open discussions on topics such as plagiarism and the responsible use of AI detection tools to educate students about the responsible use of Gen AI and the consequences of misuse in terms of academic misconduct. Shay (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) argues the importance of ensuring transparency and accountability in the integration of Gen AI in nursing education, advocating for transparency through the disclosure of AI tool usage in assignments to promote accountability. Furthermore, Archibald \u0026amp; Clark (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), Alghamdi et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Benfatah et.al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lane et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), Lagadec et. al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Simms (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) argue that institutional policies must move beyond punitive policy models, instead promoting transparency and accountability to encourage a culture of trust and ethical behaviour, which reduces the temptation for dishonest practices.\u003c/p\u003e\n \u003cp\u003eConcurrently, Sun et al. (2023) and DeGagne (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) argue that promoting critical thinking and ethical awareness plays a pivotal role in combating over-reliance on Gen AI. Yang (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Migdadi et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasise the importance of integrating AI ethics into nursing curricula to address concerns and ensure its responsible use. Ahmed et al. (2024) and El-Sayed et al. (2025) emphasise the need for more comprehensive training and exposure to Gen AI tools to enhance digital literacy and confidence among nursing students. Sun et al. (2023) propose assignments encouraging self-reflection and independent learning, emphasising tasks that require critical evaluation of Gen AI-generated content. DeGagne (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) similarly emphasises the importance of values clarification in preparing nursing students for AI\u0026apos;s ethical challenges, ultimately fostering ethical awareness and the development of critical thinking skills necessary for navigating AI in healthcare.\u003c/p\u003e\n \u003cp\u003eSrinivasan et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) recommend involving diverse stakeholders in the development of Gen AI systems to align with ethical and social values in nursing. Addressing bias, as noted by Srinivasan et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), entails training AI systems on diverse datasets to support an equitable educational experience. Lim (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) warns against linguistic bias, advocating for inclusivity in access to Gen AI tools, especially for non-native English speakers. Furthermore, Park et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) argue that educators must be conscious of the equitable access and cost of Gen AI tools, as well as the variance in outputs observed between paid and free versions of platforms such as ChatGPT, to avoid educational inequalities. In parallel with these initiatives, Simms (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Topaz et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) stress the critical role of human oversight in ensuring that the use of Gen AI in nursing education enhances, rather than replaces conventional teaching approaches and advocate for the establishment of ethical frameworks to steer this integration, as highlighted by DeGagne et al. (2024).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis scoping review aimed to explore the use of Gen AI as a teaching, learning, and assessment strategy in nursing education and to investigate the ethical considerations and perceptions associated with its implementation. The findings highlight the exponential growth and wide range of applications, including content creation, simulation, personalised learning and tutoring, and assessment preparation and implementation. The review findings are consistent with those observed in the education practices of other allied healthcare disciplines, including medicine (Rinc\u0026oacute;n et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), pharmacy (Mortlock \u0026amp; Lucas, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and physiotherapy (Lindb\u0026auml;ck et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), reporting an exponential growth on the implementation of Gen AI pedagogical implementation, indicating the universal investment in and impact of Gen AI on healthcare education.\u003c/p\u003e\u003cp\u003eThe current body of empirical literature on Gen AI in nursing education has predominantly focused on the application of ChatGPT. However, there is a pressing need for further research to explore other Gen AI applications, such as Claude, DeepSeek, Google AI, and Microsoft Co-Pilot, to develop a richer empirical understanding of the available tools and their potential pedagogical impact on nursing education. A study by Fenske \u0026amp; Otts (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the implementation of Elicit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://elicit.com/\u003c/span\u003e\u003cspan address=\"https://elicit.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, with particular emphasis on its accuracy, relevance, and efficiency using nursing-specific filters in conducting a literature review. While the results revealed some inaccuracies in identifying relevant papers and limitations in filtering capabilities, it is noteworthy that 26% of students preferred Elicit over traditional search databases such as PubMed and CINAHL, citing its speed and ease of use as primary advantages. The authors acknowledge the emergence of specialised Gen AI tools designed to support literature review and discovery processes. These include Scite, Research Rabbit, and Semantic Scholar, which offer advanced features such as citation context analysis, networked discovery, and AI-assisted synthesis of research findings. It is imperative that future research focuses on evaluating the accuracy, potential biases, utility, and reproducibility of these platforms. Additionally, comparative studies should be conducted to assess their performance and impact on research workflows in relation to traditional database searches and more general-purpose AI models, such as ChatGPT.\u003c/p\u003e\u003cp\u003eIn this review several papers (Byrne (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), Castonguay et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Irwin et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Lagadec et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Miao et al. (2023), Ni et al. (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), O'Connor (2023), Quattrini et al., (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Reed (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Shay (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Topaz et al. (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and Thakur et al. (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)), discussed the onus on nurse educators to reframe traditional assessment strategies that rely on text based methods to competency-based clinical skills, performance tasks, problem-based learning, and increased use of oral assessments like presentations, debates, and mini-vivas to safeguard against the misuse of Gen AI in formal summative assessments. While the wider literature also recommends this approach (Le, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nikolopoulou, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Uanachain \u0026amp; Aouad, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), others argue that the use of Gen AI should be incorporated in assessment strategies, but most importantly, the disclosure of the use of Gen AI is paramount (Cotton et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Khlaif, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xia et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One such approach is the incorporation of the Gen AI Assessment Scale (Perkins et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which outlines five levels of AI integration in assessments, ranging from no AI use to full AI collaboration. It describes how AI can be utilised for idea generation, editing, task completion, and as a co-pilot, with varying requirements for citation and disclosure of AI-generated content across the different levels. However, for faculty to design assessments in the context of AI, it is imperative that they first familiarise themselves with AI tools and their capabilities (Ng \u0026amp; Phua, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis review highlights that research on the impact of integrating Gen AI tools on student performance, confidence, and satisfaction is promising, yet limited. The studies included suggest that the use of Gen AI in nursing education generally leads to significant improvements in student performance, confidence and satisfaction. Notable enhancements have been observed in areas such as case management, knowledge assessments and simulation-based learning. These findings are consistent with those of Pham et al. (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who recently conducted a systematic review examining the impact of Gen AI on health professional education in relation to student learning. This review found that Gen AI has a positive impact on various aspects of student learning, including knowledge acquisition, inquiry, practice, and production, through various pedagogical approaches that incorporate Gen AI. These approaches clarify concepts, support rapid inquiry, simulate clinical practice, facilitate academic production, and foster discussion and collaboration. However, the impact on student performance, confidence, and satisfaction can vary depending on the specific Gen AI tools used and the context.\u003c/p\u003e\u003cp\u003eMany identified studies also underlined concerns about technical issues and the need for educator oversight to improve students' performance through the ease of use and effectiveness of Gen AI systems. However, Holzinger et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) argue that the future of human oversight in AI demands interdisciplinary technical and policy reform, embedding explainability and human-in-the-loop design, developing scalable monitoring and validation tools, harmonising international governance and standards, and investing in training while prioritising equity, transparency, and accountability to ensure responsible, trustworthy AI systems. The need for clear institutional policies, comprehensive training, stakeholder engagement, and accountability measures, including the disclosure of AI use, is expressed to guide the responsible and equitable deployment of AI tools in nursing education. A recent systematic review (Garc\u0026iacute;a-L\u0026oacute;pez \u0026amp; Trujillo-Li\u0026ntilde;\u0026aacute;n, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlighted the need to design inclusive regulatory frameworks, enhance digital literacy, and integrate Gen AI tools with constructivist and self-directed learning models. Based on the available evidence, the authors believe that this will ultimately improve student satisfaction and confidence in the use of Gen AI systems.\u003c/p\u003e\u003cp\u003eFrom an ethical perspective, several considerations emerged from the identified evidence regarding the integration of Gen AI in nursing education. Key considerations include ensuring privacy and transparency in data collection and AI use, in addition to preventing and mitigating biases and disparities in access or language by utilising diverse datasets and inclusive policies. The importance of human oversight and critical evaluation in managing and responding to AI-generated hallucinations and inaccuracies cannot be overemphasised to help preserve academic integrity. Furthermore, the importance of avoiding overreliance on AI is outlined, as it could undermine empathy, interpersonal skills, and independent critical thinking.\u003c/p\u003e\u003cp\u003eThe development and implementation of Gen AI in nursing education presents challenges that must be addressed through well-defined ethical frameworks. A systematic review by Fu \u0026amp; Weng (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified key ethical principles for framing responsible, human-centred AI practices in education, including (1) Fairness \u0026amp; Equity, (2) Privacy and Security, (3) Agency \u0026amp; Autonomy, (4) Transparency \u0026amp; Intelligibility, and (5) Non-maleficence \u0026amp; Beneficence. Additionally, Cherner et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have developed an ethical decision tree for introducing Gen AI in higher education based on established international frameworks and guidelines. Educators are advised to (1) identify their needs, (2) learn about the tool\u0026rsquo;s capabilities, terms, and training data, followed by (3) a Layer 1 yes/no assessment of local and societal impacts, and (4) a Layer 2 review of disclosures, policies, and outputs, considering contextual factors.\u003c/p\u003e\u003cp\u003eCherner et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) also recommend treating policy and AI literacy as contextual supports rather than evaluative criteria when implementing Gen AI. Developing AI literacy among nursing students is crucial for addressing educational inequalities (Hoelscher \u0026amp; Pugh, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). AI literacy helps level the playing field by equipping students from diverse backgrounds with the skills needed to access, utilise, and benefit from AI tools, ensuring that advantages are not confined to those with prior technology exposure or resources. It also enables a critical assessment of bias, inaccuracies, and ethical privacy risks, while preserving clinical reasoning and empowering students to advocate for equitable institutional AI policies (Simms, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although many higher education institutions worldwide have implemented AI policies to address concerns over academic integrity and provide governance for the ethical use of AI in teaching and assessment, there is still much to be learned as technology unfolds and how it can be effectively valued in the nursing education context.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eKnowledge Gaps in Current Evidence-Base\u003c/h2\u003e\u003cp\u003eWhile interest in integrating Gen AI into nursing education is on the rise, empirical research in this area remains scarce. Many discussion papers explore potential applications; only a small number of empirical studies assess their direct impact on learning outcomes, and few of these are randomised controlled trials. Further empirical research is needed to evaluate the effectiveness of these policies in preventing misconduct, shaping student behaviour, and ensuring equitable educational outcomes. A notable limitation of the research to date in the field is its reliance on quantitative surveys and self-reported data, which can introduce bias and make it challenging to draw causal conclusions about the effectiveness of Gen AI in enhancing student performance. Additionally, the small sample sizes of these studies further restrict the generalizability of their findings. Moreover, much of the literature prioritises students' perceptions over measurable academic or clinical competencies, offering limited insight into how Gen AI can effectively enhance practical nursing education. Although Gen AI is rapidly emerging in nursing education, the evidence base remains early and fragmentary. To date, there are no robust longitudinal studies, and this scoping review did not identify any current postgraduate research projects (PhD or Master's level). This may be because PhD programs typically take 4\u0026ndash;6 years to complete, and the technology is still relatively new. It is likely that empirical research is limited because the formal adoption of Gen AI and its potential risks to assessment integrity are still emerging challenges for educational institutions worldwide. Educators are predominantly awaiting further research and real-world case studies to better understand whether and how Gen AI influences academic integrity and overall educational outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations of the Scoping Review\u003c/h2\u003e\u003cp\u003eThis scoping review has several strengths and limitations. The literature search was conducted in collaboration with a subject librarian, who utilised several databases and extensive grey literature sources. The review is the first of its kind to include the broader breadth of grey literature in conjunction with empirical research.\u003c/p\u003e\u003cp\u003eThe review only included publications written in English, which potentially excluded relevant studies published in other languages. The greater focus was on published literature, which may have introduced a risk of publication bias. Additionally, the review excluded studies on Gen AI applications outside the field of nursing education, potentially missing important interdisciplinary insights and transferable findings from other fields in healthcare education. Furthermore, given the rapid development of Gen AI, some recent advances or ongoing discussions may not be fully represented in this review. These limitations should be considered when interpreting the findings, emphasising the need for ongoing research in this evolving field.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile Gen AI is transforming nursing education by offering innovative tools for teaching, learning, and assessment, this review offers a novel mapping of the attitudes towards and ethical considerations of Gen AI, which include privacy, bias, academic integrity, and the potential for dependency. These may be addressed through clear institutional policies, comprehensive training and educator oversight. Future research should focus on evaluating the long-term impact of Gen AI on nursing education, developing robust ethical frameworks, and ensuring equitable access to AI tools and resources. Finally, prioritising AI literacy among students and educators is essential, as it fosters critical thinking and ethical awareness to navigate the complexities of AI integration.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGenerative Artificial Intelligence: Gen AI\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: Not applicable\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eThis scoping review was developed with contributions from all authors as detailed below:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualisation and Design:\u003c/strong\u003e {PH, AD, RD, JEC, SK, BMB, MM}\u0026nbsp;were responsible for the initial conceptualisation and design of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e {PH, AD, RD, JEC, SK, BMB, MM}\u0026nbsp;developed the methodology and the review criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiterature Search and Data Extraction:\u003c/strong\u003e {PH, JEC, AS, DZ} conducted the initial literature search and were responsible for data extraction and management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrafting of the Manuscript:\u003c/strong\u003e {PH, MM} drafted the first version of the manuscript. All authors provided substantial revisions and improvements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCritical Review and Editing:\u003c/strong\u003e {PH, AD, RD, JEC, SK, BMB, MM, AS, DZ} critically reviewed the manuscript for important intellectual content and provided necessary revisions.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work. PH is the guarantor.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: Not Applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdel-Moaty, M. A. A., El-Molla, M. A., \u0026amp; Abdel-Wahab, E. A. (2024). Nursing interns\u0026rsquo; perception about artificial intelligence applications in nursing. Egyptian Nursing Journal, 21(2), 121\u0026ndash;128. https://doi.org/10.4103/enj.enj_19_24\u003c/li\u003e\n\u003cli\u003eAbdulai, A. F., \u0026amp; Hung, L. (2023). 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Journal of clinical nursing, 33(6), 2362\u0026ndash;2363. https://doi.org/10.1111/jocn.17089\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKeywords for each search string \u0026amp; database\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation (P): Nursing students\u003c/p\u003e\n \u003cp\u003eEMBASE: \u0026apos;nursing student\u0026apos;/exp\u003c/p\u003e\n \u003cp\u003eMedline (MH \u0026quot;Students, Nursing\u0026quot;) OR\u003c/p\u003e\n \u003cp\u003eWeb of Science topic search with keywords\u003c/p\u003e\n \u003cp\u003eCINAHL: (MH \u0026quot;Students, Nursing+\u0026quot;) OR (MH \u0026quot;Students, Pre-Nursing\u0026quot;) OR (MH \u0026quot;Students, Nursing, Baccalaureate+\u0026quot;) OR (MH \u0026quot;Students, Nursing, Practical\u0026quot;)\u003c/p\u003e\n \u003cp\u003eApplied Social Science index and abstract: MAINSUBJECT.EXACT.EXPLODE(\u0026quot;Nurses\u0026quot;) AND MAINSUBJECT.EXACT.EXPLODE(\u0026quot;Undergraduate students\u0026quot;)\u003c/p\u003e\n \u003cp\u003eERIC: MAINSUBJECT.EXACT.EXPLODE(\u0026quot;Nursing Students\u0026quot;)\u003c/p\u003e\n \u003cp\u003eKeywords on title and abstract: \u0026ldquo;Student Nurs*\u0026rdquo; OR \u0026ldquo;Trainee Nurs*\u0026rdquo; OR \u0026ldquo;Nursing Trainee*\u0026rdquo; OR \u0026ldquo;Nurs* Intern*\u0026rdquo; OR \u0026ldquo;Nursing Learner*\u0026rdquo; OR \u0026ldquo;Novice Nurse*\u0026rdquo; OR \u0026ldquo;Undergraduate Nurse*\u0026rdquo; OR \u0026ldquo;Postgraduate Nurse*\u0026rdquo; OR \u0026ldquo;Nursing student*\u0026rdquo; OR \u0026ldquo;baccalu* nurs*\u0026rdquo; OR \u0026ldquo;nursing novice*\u0026rdquo; OR \u0026ldquo;nursing mentee*\u0026rdquo;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(AND)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcept (C)\u003c/strong\u003e: AI\u003c/p\u003e\n \u003cp\u003eEMBASE: \u0026apos;artificial intelligence chatbot\u0026apos;/exp OR \u0026apos;virtual assistant\u0026apos;/exp OR \u0026apos;artificial intelligence\u0026apos;/exp\u003c/p\u003e\n \u003cp\u003eMedline: (MH \u0026quot;Artificial Intelligence+\u0026quot;) OR (MH \u0026quot;Machine Learning+\u0026quot;)\u003c/p\u003e\n \u003cp\u003eCINAHL: (MH \u0026quot;Artificial Intelligence+\u0026quot;) OR (MH \u0026quot;Artificial Intelligence, Generative\u0026quot;)\u003c/p\u003e\n \u003cp\u003eApplied Social Science Index \u0026amp; Abstracts: MAINSUBJECT.EXACT.EXPLODE(\u0026quot;Artificial intelligence\u0026quot;)\u003c/p\u003e\n \u003cp\u003eWeb of Science: topic search on keywords\u003c/p\u003e\n \u003cp\u003eERIC: MAINSUBJECT.EXACT.EXPLODE(\u0026quot;Artificial Intelligence\u0026quot;)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKeywords on title and abstract\u003c/strong\u003e: \u0026ldquo;Generative Artificial Intelligence\u0026rdquo; OR \u0026ldquo;Gen AI\u0026rdquo; OR \u0026ldquo;Artificial Intelligence\u0026rdquo; OR \u0026ldquo;Computational Intelligence\u0026rdquo; OR \u0026ldquo;Automated Reasoning\u0026rdquo; OR \u0026ldquo;Algorithmic Intelligence\u0026rdquo; OR \u0026rdquo;Synthetic Intelligence\u0026rdquo; OR \u0026ldquo;Machine Learning\u0026rdquo; OR ChatGPT OR Chatbot* OR \u0026ldquo;Conversational AI\u0026rdquo; OR \u0026ldquo;AI Assistant*\u0026rdquo; OR \u0026ldquo;Virtual Assistant*\u0026rdquo; OR \u0026ldquo;Intelligent Chatbot\u0026rdquo; OR \u0026ldquo;Automated Chat Agent*\u0026rdquo; OR avatar OR \u0026ldquo;Virtual Chat Companion*\u0026rdquo; OR \u0026ldquo;Dialogue System\u0026rdquo; OR \u0026ldquo;large language model*\u0026rdquo; OR \u0026ldquo;Learning Management System*\u0026rdquo; OR \u0026ldquo;AI-Powered\u0026rdquo; OR \u0026ldquo;Notebook LM\u0026rdquo; OR \u0026ldquo;Google AI\u0026rdquo; OR \u0026ldquo;AI\u0026rdquo; OR \u0026ldquo;sentient analys*\u0026rdquo; OR Grammarly OR Duolingo OR \u0026ldquo;Microsoft Co-Pilot\u0026rdquo; OR Scite OR \u0026ldquo;Research Rabbit\u0026rdquo; OR \u0026ldquo;Semantic Scholar\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(AND)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eContext (Co)\u003c/strong\u003e: Nursing Education\u003c/p\u003e\n \u003cp\u003eEMBASE: (\u0026apos;nursing education\u0026apos;/exp OR \u0026apos;nurse training\u0026apos;/exp OR \u0026apos;pedagogics\u0026apos;/exp)\u003c/p\u003e\n \u003cp\u003eMedline: (MH \u0026quot;Education, Nursing\u0026quot;) OR (MH \u0026quot;Education, Nursing, Baccalaureate\u0026quot;) OR (MH \u0026quot;Preceptorship\u0026quot;)\u003c/p\u003e\n \u003cp\u003eCINAHL: (MH \u0026quot;Education, Nursing\u0026quot;) OR (MH \u0026quot;Nurse Educators\u0026quot;) OR (MH \u0026quot;Education, Nursing, Baccalaureate+\u0026quot;) OR (MH \u0026quot;Education, Nursing, Practical\u0026quot;) OR (MH \u0026quot;Entry Into Practice\u0026quot;) OR (MH \u0026quot;Internship and Residency\u0026quot;)\u003c/p\u003e\n \u003cp\u003eApplied Social Science Index \u0026amp; Abstracts: no appropriate index terms\u003c/p\u003e\n \u003cp\u003eWeb of Science: topic search on keywords\u003c/p\u003e\n \u003cp\u003eERIC: MAINSUBJECT.EXACT.EXPLODE(\u0026quot;Nursing Education\u0026quot;)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKeywords on title and abstract\u003c/strong\u003e: \u0026ldquo;Nursing Educat*\u0026rdquo; OR \u0026ldquo;Nursing Train*\u0026rdquo; OR \u0026ldquo;Nursing Studies\u0026rdquo; OR \u0026ldquo;Nursing Curricul*\u0026rdquo; OR \u0026ldquo;Nursing Pedagog*\u0026rdquo; OR \u0026ldquo;Nursing exam*\u0026rdquo; OR \u0026ldquo;Nursing Teaching*\u0026rdquo; OR \u0026ldquo;Undergraduate Nurs*\u0026rdquo; OR \u0026ldquo;Post-Graduate Nurs*\u0026rdquo; OR \u0026ldquo;PG Nurs*\u0026rdquo; OR \u0026ldquo;UG Nurs*\u0026rdquo; OR \u0026ldquo;postgraduate nurs*\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Discussion/Opinion/Conference Papers Examining Teaching, Learning and Assessment Applications of Gen AI in Nursing Education\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eAuthor, Year of Publication\u003c/p\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003ePublication Type:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eGen AI Teaching Learning Assessment Applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eEthical Considerations of the Application of Gen AI in Nursing Education\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eAbdulai, et. al. (2023)\u003c/p\u003e\n \u003cp\u003eCanada\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eCommentary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eDiscuss how the use of ChatGPT may undermine the values, principles, and core assumptions that underpin nursing research, education, and practice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eChatGPT\u0026apos;s inability to ensure confidentiality and its reductionist approach pose significant challenges to nursing\u0026apos;s holistic, empathy-driven care model and ethical responsibilities. Raises concerns about academic integrity and the potential development of nurses lacking critical thinking skills and core nursing values.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eAlharbi (2024)\u003c/p\u003e\n \u003cp\u003eSaudi Arabia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eGen AI can enhance simulation experiences by offering tailored, realistic scenarios, such as AI-driven robots that provide more authentic interactions than traditional simulators.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eDid not discuss ethical considerations.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eArchibald \u0026amp; Clark (2023)\u003c/p\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT enhances education by generating diverse content, creating interactive tutors, automating grading, and personalising online learning experiences, saving time for educators while improving effectiveness for students.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eEducators play a vital role in promoting ethical conduct and academic integrity among students. Institutions, in turn, must provide AI ethics training, establish clear policies, and implement ongoing risk assessment strategies to ensure the responsible integration of ChatGPT in education.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eBumbach (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT chatbot and retrieve an output. It can be used for answering questions, searching for information, generating essays, and articles in a conversation between the user and the program.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eEducators can resist AI or, alternatively, proactively incorporate this technology while employing it with evidence-based information, ethics, morals, and a professional approach to its use.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eByrne (2025)\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eArtificial Intelligence building and delivering an educational experience requires careful matching of learning objectives with teaching/learning activities.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eDifficulty in detecting academic misconduct should not overshadow subtle or embedded biases. Bias in data used for training or design in an AI system may disenfranchise certain students.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eCastonguay, et. al. (2023)\u003c/p\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT is an AI-enabled text generator that can engage in conversations and answer questions.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eGen AI has limitations in terms of the accuracy of its outputs and potential biases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eChan, et. al. (2023)\u003c/p\u003e\n \u003cp\u003eChina\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT can generate interview questions and nursing interventions.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eChatGPT should reflect an emphasis on the importance of nursing actions and incorporate a holistic and humanistic approach.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eChen (2024)\u003c/p\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT enhances education through personalised learning, user-friendly interfaces, quick information access, writing and problem-solving assistance, and supports educators in curriculum development by generating teaching cases and simulating clinical scenarios.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eGen AI may limit students\u0026rsquo; critical thinking, problem-solving, and innovation capabilities, leading to a lack of independent thought.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eDante, et. al. (2022)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eItaly, USA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eGen AI can be utilised to programme High-Fidelity Patient Simulators (HFPS)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eDid not discuss ethical considerations.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eDeGagne (2023)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eAdvanced technologies like predictive analytics, VR simulations, computer vision, and natural language processing enhance medical data analysis, reduce errors, improve patient care, and offer interactive learning tools like virtual avatars and chatbots to enrich nursing education.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eEarly integration of values clarification in nursing education prepares students to address AI-related ethical challenges in healthcare, fostering critical thinking and ethical awareness around issues like privacy, equity, and patient-centered care.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eDegagne et.al. (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA, Korea\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eAI, Turnitin32 or GPTZero. Gen AI can provide realistic simulations of clinical scenarios to help students practice clinical decision-making skills in a secure environment.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eProposes five ethical principles (autonomy, nonmaleficence, beneficence, justice, and explicability) as a framework for responsibly integrating Gen AI in nursing education, addressing ethical challenges to protect students, maintain standards, and promote patient-centred care.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eLim (2023)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eEditorial Discussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eMachine-generated writing tools like ChatGPT can be utilised in education for student tutoring, stimulating dialogue, and as memory aids.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eThere may be a bias associated with ChatGPT, as students with English as an additional language might be more likely to be suspected of using AI.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eForonda\u0026nbsp;\u0026amp; Porter (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT. AI involves computer programs that simulate qualities of the human mind, such as the ability to process language, recognise pictures, solve problems,\u003cbr\u003e\u0026nbsp;and learn.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eCaution over the use of AI can lead to bias, injustice, inequities, and inaccuracies.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGapp et.al. (2025)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eOpen artificial intelligence (AI) platforms in nursing education can be beneficial when given the correct prompts to use.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eDid not discuss ethical considerations.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGehring, et. al. (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eAI, ChatGPT, Generative Artificial Intelligence, Large Language Models. ChatGPT 4 produces more accurate written essays with citations, provides correct answers to open-ended questions, presents valid research data, and achieves improved pass rates on professional competency exams.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eAcademic nurse educators should consider incorporating GenAI tools into their educational practices. Some are willing, while others have concerns over ethical implications, such as academic integrity.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGhane, et., al, (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIran\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eEditorial Discussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eArtificial Intelligence (AI). A primary application of AI in nursing is patient monitoring. AI‐powered monitoring systems can continuously collect and analyse patient data.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eData privacy and security must be maintained, and nurses need adequate training to use AI tools effectively. There needs to be a balance between human interaction and AI use in hospitals.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGonzalez (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT, a conversational AI trained on diverse text, generates human-like, context-aware responses and can enhance nursing education by supporting teaching, personalised learning, and critical thinking.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eConcerns over potential biases, user privacy, transparency, and psychosocial considerations. It is important for nurse educators considering the use of AI technology in curricula to have awareness of these concerns and possible actions to mitigate them in practice\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eGosak, et. al. (2024)\u003c/p\u003e\n \u003cp\u003eUSA, Slovenia \u0026amp; UK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT. Chatbots can be utilised in problem-based learning to provide nurses with practical experience.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eChatGPT can assist nurses with patient problems, medical histories, and care planning, but its outputs may not align with the North American Nursing Diagnosis Association-International (NANDA-I), highlighting the need for professional oversight.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eHarrison (2024)\u003c/p\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT is a chatbot that responds to questions by reviewing vast quantities of data previously generated by humans.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eConcerns over privacy, security, lack of accountability for errors. Also, exacerbates digital inequalities and there is a loss of human interaction.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eIrwin et. al. (2023)\u003c/p\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eEditorial Discussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT is trained on large amounts of text data and can generate human-like content in response to user prompts with high levels of accuracy.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eAssessment methods that rely on text-based responses prepared and uploaded individually by the student may compound academic integrity concerns.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eJung (2023)\u003c/p\u003e\n \u003cp\u003eKorea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eArtificial Intelligence (AI) can provide improved realism, engagement, and personalisation in nursing simulation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eConcerns over the use of AI include autonomy and patient privacy. \u0026nbsp;AI may redefine educators\u0026apos; roles and may necessitate them to teach students ethical decision-making.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eKelarijani et.al. (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT from OpenAI provides comprehensive, logical textual responses to assist nursing students and instructors with academic questions, assignments, and research projects.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eReliance on the use of AI can lead to problems in developing essential skills in nursing students. These skills include critical thinking, clinical reasoning, the ability to design a nursing care plan, and problem-solving skills\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eLagadec, et. al. (2024)\u003c/p\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eEditorial Discussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT draw on a vast set of data, synthesising information from multiple sources and can significantly impact nursing education and practice. Unlike traditional computer technology, AI has the capacity to think, learn, perceive, and make rational decisions without human intervention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eUsing AI in assessment tasks raises concerns of academic integrity, plagiarism or even fraud if the AI system is not acknowledged as the source of information.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eLiu, et. al., (2023)\u003c/p\u003e\n \u003cp\u003eChina, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eEditorial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT, ChatGPT 3.5. ChatGPT has been found to be particularly useful in the field of education, as it can provide coherent and contextual answers to questions.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eConcerns over patient privacy, informed consent and maintaining professional boundaries.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eMaykut et.al. (2024)\u003c/p\u003e\n \u003cp\u003eCanada\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT, AI, OpenAI, general AI, chatbot. Students were introduced to the idea of integrating and analysing a ChatGPT response. The response was utilised as an initial brainstorming idea to expand the students\u0026rsquo; knowledge of concepts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eResistance to AI integration stems from faculty unpreparedness, fear of the unknown, and ethical concerns, necessitating a balanced approach that exposes students to diverse technologies, teaches critical understanding of ethical implications, and guides integration through a humanistic framework centred on ethical and inclusive practices.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eMiao, et. al., (2023)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eEditorial Discussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT, ChatGPT-4, Artificial Intelligence, OpenAI. Recent use of ChatGPT in various scientific questions, like intelligent transportation, drug discovery and nursing education, research, and practice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eConcerns over plagiarism. However, the ban on ChatGPT may not last long, as other companies, such as Microsoft, are integrating ChatGPT into their Office products.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eNi et.al. (2024)\u003c/p\u003e\n \u003cp\u003eChina\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eArtificial intelligence, ChatGPT, ChatGPT 3.5, ChatGPT 4.0, \u0026nbsp;Large Language Models (LLMs), GPTZero, AI Text-Classifier, and ChatGPT Detector. ChatGPT software has been successfully used to pass the American Heart Association (AHA) Basic Life Support (BLS) test and the Plastic Surgery In-Training Exam (PSITE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eChatGPT\u0026rsquo;s reliance on offline data can lead to fabricated references, raising concerns among scientists about its impact on scientific transparency and ethical integrity in medical research.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eO\u0026apos;Connor (2021)\u003c/p\u003e\n \u003cp\u003eIreland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eGen AI is hailed as a way to solve problems that impact health professionals, patients, students, and educators by improving the speed and accuracy of available information.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eThere are concerns over productivity-oriented solutionism with virtual teacherbots, when higher education should be grounded in humanism.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eO\u0026rsquo;Connor (2023)\u003c/p\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eEditorial Discussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eOpenAI, ChatGPT 3.5. The Reflection (PAIR) framework, which outlines how to utilise gen AI tools, including the development and application of prompts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eEducators and students may be concerned about fairness in accessing and using Gen AI tools, particularly when these tools are used to help students learn information and prepare assignments.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eO\u0026apos;Connor, et. al. (2023)\u003c/p\u003e\n \u003cp\u003eUK and Canada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT is a powerful generative AI tool that uses algorithms to process large volumes of digital data, text, images, audio, and video and generate new content based on user input.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eThe ethical use of AI-detection tools in education requires open faculty discussion, clear policies, and student transparency to address issues like false positives. Other concerns include threats to privacy, security, copyright, bias, and inaccuracy.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eO\u0026apos;Connor, et.al. (2024)\u003c/p\u003e\n \u003cp\u003eUK, Canada, Hong Kong, Slovenia, US, Taiwan, Finland.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eA variety of GenAI tools, including image, auditory, text, video, large language models, and prompt engineering. ChatGPT can be used to help with unfolding case studies and role-playing exercises.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eThere may be inaccurate or biased content which can be created by AI. Therefore, there should be a critical evaluation of AI-generated content.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003ePizzulo (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eOpenAI, Artificial Intelligence (AI). ChatGPT helps to spark initial ideas. When teaching a topic without specific thoughts on engaging activities, it can generate a list of items.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eFactual errors due to ChatGPT\u0026apos;s inability to comprehend a question, delayed updates on information, potential misuse, limited access to databases and biased responses.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eQuattrini et al., (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT provides instant responses to prompts, supporting tasks like writing essays, generating exam questions and producing clinical documentation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eMany educators are wary of AI\u0026rsquo;s ethical risks, particularly the potential for student misuse of ChatGPT. Therefore, leading to reluctance in embracing it as a learning tool.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eReed (2023)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChat-based tools like ChatGPT and Bing Chat can be utilised for educational purposes.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eThere are concerns, mostly due to cheating. With AI on the rise, nurse educators must find ethical ways to integrate it into their educational practices.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eSharpnack (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eAI technologies are vital for enhancing nursing education, serving as research assistants, simulating patient interactions, supporting communication, developing care plans, and enabling immersive problem-based and experiential learning to strengthen students\u0026apos; clinical skills, decision-making, and critical thinking.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eData privacy and the need for comprehensive training to effectively interpret and employ AI-driven tools remain pivotal aspects to address when integrating AI into nursing education.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eShay (2023)\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eAdvancements in AI highlight the need for nursing educators to understand their potential and limitations, as the technology has demonstrated capabilities in healthcare tasks, including passing exams and generating patient notes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eConcerns exist around academic integrity and reliance on AI tools. Institutions recommend transparent use, disclosing the tool, sharing work processes, rather than generating complete assignments, focusing on established plagiarism detection methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eSilvestri-Elmore \u0026amp; Burton (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eApplying AI, such as ChatGPT, to develop unfolding case studies in nursing education can promote active learning, enhance clinical judgment, and reduce barriers to implementing this strategy, thereby supporting clinical learning and critical thinking skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eDid not discuss ethical considerations.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eSimms, (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT provides personalised, interactive learning experiences. The integration of AI is supported by constructivist theories and Vygotsky\u0026rsquo;s Zone of Proximal Development, which emphasise AI\u0026rsquo;s role in enhancing learners\u0026apos; development through tailored support and scaffolding.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eGen AI tools challenge academic integrity, pose a challenge to validating information accuracy, and require strategies to ensure the credibility of AI-generated information.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eSimms (2025)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eEmphasises the importance of educators adapting curricula and teaching methods to effectively integrate generative AI learning, ensuring students are proficient in Gen AI technologies and aware of their ethical implications.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eIntegrating AI in nursing education requires addressing ethical concerns around privacy, bias, transparency, and accountability through responsible use policies, human oversight, and critical evaluation, while using AI to supplement, not replace, traditional teaching and leveraging AI mistakes as learning opportunities to enhance students\u0026apos; critical thinking and responsible technology use.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eSrinivasan, et. al. (2024)\u003c/p\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eThe use of chatbots like ChatGPT in nursing education offers benefits such as personalised learning, enhanced engagement, and increased efficiency, but also poses challenges and ethical concerns that need to be addressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eRaises concerns about bias, privacy, security, accountability, and transparency, which can be addressed through comprehensive training data, strong privacy protections, stakeholder involvement, and ethical alignment to ensure a balanced, inclusive student preparation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eSun \u0026amp; Hoelscher (2023)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT, an artificial intelligence-driven, pretrained, deep learning language model, can generate natural language text in response to a given query. Its rapid growth has raised concerns about its ethical use in academia.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eEthical challenges due to biases in training data can impact their accuracy. To address this, faculty should focus on designing assignments that promote self-reflection, critical thinking, and independent learning, while also teaching students to critically evaluate information and make informed decisions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eTopaz et al. (2025)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eGen AI helps nursing students by organising ideas, generating scenarios and realistic simulations, personalising tutoring and feedback, curating accessible learning materials, and automating grading to save instructors\u0026apos; time.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eKey risks include threats to academic integrity, bias, privacy, and copyright/plagiarism issues, as well as AI \u0026quot;hallucinations,\u0026quot; which require fact-checking, ethical-use policies, and student education on the limits of AI.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eThakur et al. (2023)\u003c/p\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatGPT, Chatbot, OpenAI, mobile chatbot. Chatbots are an emerging AI application that simulates dialogue through audio or text and is capable of performing complex tasks, such as interacting with human users.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eAlthough not explicitly stated, the authors conclude that there have been concerns that ChatGPT may be misused.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2796%;\"\u003e\n \u003cp\u003eZhou \u0026amp; Mui (2024)\u003c/p\u003e\n \u003cp\u003eMalaysia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eDiscussion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5054%;\"\u003e\n \u003cp\u003eChatbot in nursing education offers a safe setting for nursing students to practice their skills. These chatbots simulate real-life clinical situations and interactions with patients.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.9892%;\"\u003e\n \u003cp\u003eWhile AI can increase accessibility for diverse students, it raises ethical and privacy concerns, risks of overreliance that may weaken critical thinking, and has accuracy and depth limitations for complex or specialised nursing scenarios.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Empirical Research Examining Teaching, Learning and Assessment Applications of Gen AI in Nursing Education\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eAuthor, Year of Publication\u003c/p\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eStudy Aim(s)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eResearch Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003eMethodology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eGen AI TLA applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eEthical Considerations\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eResults\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eAkutay, et.al., (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTurkey\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo evaluate the impact of AI on nursing students\u0026rsquo; in-class case analysis lectures on their case management performance and satisfaction.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eRandomised Controlled Trial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (188) Third-year nursing students: AI group (n=94 students), control group (n=94 students). Students\u0026apos; case management and diagnoses were assessed using various tools, and lecture satisfaction was measured using a Visual Analogue Scale (VAS).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eAI: DALL-E3 Image-Gen to create images based on researcher-written prompts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eAI group students scored higher in case management and knowledge tests, with no difference in satisfaction. AI-generated visual narratives can improve nursing education.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eAthilingam \u0026amp; He (2024)\u003c/p\u003e\n \u003cp\u003eUSA, Singapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eThis paper presents responses generated by ChatGPT in response to the authors\u0026rsquo; prompt on nursing education, teaching, and learning.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eAn exploratory, qualitative design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (2) Nurse Educators: Given prompts for ChatGPT on nursing education, teaching, and learning. The authors provided evidence to support each response on the benefits, challenges, and controversies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eThree prompts explored the background, methods, benefits, and ethical considerations of using ChatGPT in education and student assignments.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003ePlagiarism, unfair advantage, bias, inaccuracies, data privacy and security issues, and a lack of accountability and responsibility.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eChatGPT offers significant potential to enhance teaching and learning through personalised education, virtual tutoring, and automated feedback, but its use raises ethical concerns such as plagiarism, bias, and reduced critical thinking, necessitating clear policies and further research for responsible integration in nursing education.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eBasaran \u0026amp; Duru, (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo investigate the impact of Kahoot, ChatGPT, and traditional teaching methods on nursing students\u0026rsquo; sexual health knowledge and attitudes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQuasi-Experimental Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (93) nursing students using an online survey. pre-test, a post-test, and a follow-up test. The Sexual Health Knowledge Test \u0026amp; The Sexual Health Attitude Scale for Youth measured students\u0026rsquo; levels of sexual health knowledge and attitudes.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eChatGPT as a teaching method for sexual health knowledge and attitudes [does not provide specifics of how this was achieved]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eAll methods boosted confidence; ChatGPT had the strongest impact. Kahoot and ChatGPT improved knowledge more, while traditional methods better influenced attitudes. Overall, all methods had similar effects over time.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eBenfatah et.al., (2024)\u003c/p\u003e\n \u003cp\u003eMorocco\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo investigate the impact of\u003cbr\u003e\u0026nbsp;AI-assisted debriefing on the clinical development of nursing students compared to conventional debriefing techniques.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eA Comparative Study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (40); nursing students; an experimental group receiving AI-assisted debriefing and a control group receiving debriefing without AI.\u0026nbsp;\u003cbr\u003e\u0026nbsp;The study used Deepgram for audio analysis of communication and OpenPose for video analysis of gestures, assessing clinical skills and interaction quality.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eA ChatGPT-3.5-based Chatbot system facilitated AI interactions during debriefing for the experimental group.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eEnsuring student privacy, transparency, addressing bias, establishing accountability, and creating guidelines for ethical AI use.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eAI-assisted simulation and debriefing improved students\u0026apos; confidence, engagement, and critical reflection more than traditional methods, with high gesture accuracy and better integrated asthma management, although clinical understanding remained similar across groups. Increased active participation and critical reflection were observed in the experimental group.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eBenfatah et.al., (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMorocco\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo identify subtle ways in which ChatGPT can address specific challenges and enhance the overall efficacy of training programs through its use in nursing education.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eMultifaceted Exploratory Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (12) nursing students: Examined acceptability, accessibility, engagement ratings, and assessed students\u0026apos; virtual patient interaction skills. For analysis, interactions were recorded.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eParticipants in the study were assigned to the simulation individually and interacted with ChatGPT as if they were caring for a real patient to assess the usefulness of ChatGPT as a virtual patient for healthcare simulation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eStudents rated ChatGPT highly for accessibility, engagement, and usefulness. Overall performance in virtual patient interactions was good, with clarity and relevant answers being crucial for success.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eBonacaro, et. al. (2024)\u003c/p\u003e\n \u003cp\u003eItaly, Greece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eThe study examines views of nurses, educators, and students on ChatGPT\u0026apos;s potential impact on healthcare, education, and nurse-patient relationships.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eAn observational study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (176) participants, including nursing students (37.5%), nurses (32.4%), and educators (8%), who were voluntarily recruited from a university in Northern Italy. Data were collected through an online questionnaire.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eThe use of AI (ChatGPT).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eAI may reduce direct human interaction, potentially weakening nurse-patient relationships and empathy, which could impact students\u0026rsquo; ability to develop these skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eMost respondents (50-85%) view AI positively for improving nursing practice, education, and care, mainly in personalised training, cost savings, and inclusivity. They are sceptical about AI replacing staff or educators but see potential benefits in care quality, efficiency, diagnostics, and job satisfaction, especially in administrative tasks.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eChang, et. al., (2022)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTaiwan\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eThe research investigated whether a mobile chatbot enhances students\u0026rsquo; learning and self-efficacy in an obstetric vaccination course compared to traditional teaching methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eA Quasi-Experiment [Pre- and post-testing]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (36) nursing students: (n 18) experimental group using a chatbot-based learning approach and a control group (n=18) receiving traditional instruction, Pre and Post Test questionnaire and Interviews with (n 10) participants.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eThe Chatbot Disease Manager offers educational content on infectious diseases and vaccines, provides real-time COVID-19 updates, and tracks global epidemic trends to inform users about necessary travel precautions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eMost students felt the mobile chatbot improved understanding and clinical preparedness by simulating real health issues. It helped them apply knowledge and solve problems, fostering deeper learning. Adding features like image recognition could further enhance its usefulness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eChang, et. al., (2024)\u003c/p\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eImpact of a Gen AI-based art therapy approach compared to traditional art therapy instruction on students\u0026apos; empathy levels and art therapy performance.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQuasi-experimental Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (65) undergraduate nursing students used a modified empathy questionnaire and art therapy rubrics to assess empathy levels and evaluate their art therapy works based on expression, emotional depth, colour use, symbolism, and creativity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eWeek 1 - introduction to gerontological nursing and pre-tests; Week 2 - experimental group using Gen AI-based art therapy with SDL phases, while the control group used traditional digital art therapy with similar SDL stages; Week 3 - post-tests for both groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eThe experimental AI group showed significant improvements in empathy and art therapy skills, excelling in expression, colour use, symbolism, and creativity, though not in emotional depth. Analysis highlighted strong links between expression and colour, with room for growth in creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eDorin \u0026amp; Atkinson (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eFaculty\u0026apos;s understanding of AI-Natural Language Platforms and their pedagogical applications for integration into coursework.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQualitative Exploratory Research\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en (98) Participants: n (51) evaluators, n (47 ) instructors completed a survey instrument that was developed to understand current nursing faculty perceptions and knowledge of AI-NLP platforms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eArtificial intelligence-natural language processing (AI-NLP) platforms (ChatGPT OpenAI, AI-NLP classifier), their pedagogical uses and integration into coursework.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eInstitutions need guidelines to address academic integrity, emphasising fairness, genuine learning, and human involvement in education.\u003c/p\u003e\n \u003cp\u003eTop of Form\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eMost nursing faculty are unfamiliar with AI-NLP platforms and their use, but many acknowledge the need to understand their capabilities, identify student use, and integrate the technology into coursework.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eFenske \u0026amp; Otts (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eAssess students\u0026apos; ability to analyse the strengths and weaknesses of a GenAI research tool, focusing on accuracy, relevance, and efficiency.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eA descriptive, observational study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (323) graduate nursing students compared search results for their Phenomenon of Interest (POI) using PubMed, CINAHL, and Elicit (an AI tool). They analysed Elicit\u0026apos;s result generation process and selected two relevant articles for their POI.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eElicit is an artificial intelligence research assistant that utilises language models to automate part of researchers\u0026apos; workflows. Elicit utilises semantic similarity to identify articles sourced from the corpus of Semantic Scholar.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eUsers must critically evaluate AI-generated content, verify accuracy, protect privacy, avoid over-reliance, and consider equitable access while using AI tools responsibly.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eCINAHL (31.6%), PubMed (30.7%), and Elicit (26%) were the top choices among users, with CINAHL favoured for nursing-specific content, PubMed for its extensive database, and Elicit for speed and ease of use despite technical limitations. 11.8% had no preference.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eGonzalez-Garcia et al (2025)\u003c/p\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eEvaluate the impact of ChatGPT on nursing students\u0026rsquo; education and determine how it influences their learning outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQuantitative cross-sectional design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (98) nursing students: \u0026nbsp;AI knowledge, and perceptions of ChatGPT as an educational tool questionnaire. After completing the course, students evaluated ChatGPT\u0026rsquo;s effectiveness in enhancing their skills and knowledge.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eEight nursing case studies, students used ChatGPT to generate solutions before they were presented. The cases simulated real-world challenges and provided guidance on using AI for problem-solving.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eChatGPT use correlated with improved academic performance, with 89.5% of students reporting significant grade improvements. Women found it particularly helpful for academic tasks, and a positive link was observed between prior ChatGPT use and higher GPAs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eGotkas et.al. (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTurkey, Ireland\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo develop guidelines on how to employ ChatGPT 4.0 using the prompt learning method.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eCase Study \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003eIn the method of prompt learning, users provide specific prompts, and the AI model responds according to its training, specifically aligning with the progression of knowledge and skills in nursing education as outlined by Benner\u0026apos;s theory. Using ChatGPT 4.0 With Prompt Learning for Educational Purposes.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eHow to employ ChatGPT 4.0 using the prompt learning method.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eUsers must be aware of AI limitations, avoid sharing sensitive information, and craft unbiased prompts to ensure the equitable and responsible use of AI while protecting both privacy and accuracy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eThe customisation of a conversational AI chatbot shows support for the development of nursing knowledge and skills. This paper outlines how to integrate Benner\u0026apos;s theory with ChatGPT\u0026apos;s capabilities, addresses bias issues, and establishes best practices for the safe and effective use of AI in nursing.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eHan, et.al. (2022)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRepublic of Korea\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo develop and evaluate the effectiveness of an AI chatbot educational program for teaching nursing students electronic fetal monitoring skills\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQuasi-Experimental Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (61) nursing students; n = (31) control group, and n (30) experimental group examined the effect of an AI chatbot educational program on EFM skills, with the experimental group receiving both video and chatbot lectures, while the control group received only video lectures.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eAn artificial intelligence chatbot educational program for promoting nursing skills related to electronic fetal monitoring.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eThe transactional nature of the chatbot design limits its ability to provide personalised, specific feedback, which could restrict effective learning and accurate correction, raising concerns about the chatbot\u0026apos;s reliability and impact on student understanding.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eThe study found no significant differences between the experimental and control groups in terms of knowledge, clinical reasoning, confidence, feedback, or satisfaction. However, the experimental group showed significantly higher interest in education and self-directed learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eHawk, et. al., (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eExplore nursing students\u0026rsquo; perspectives on using a chatbot to answer a clinical question.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQualitative Descriptive Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003e\u0026nbsp;n = (19) nursing students. A qualitative approach, utilising reflective thematic analysis of written reflections, was employed.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eStudents used a chatbot to answer a clinical question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eRequires human oversight and critical validation of AI outputs to ensure accuracy and mitigate risks such as fabricated sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eStudents recognised the chatbot\u0026apos;s accuracy and familiarity, but stressed the need for critical validation, noting its helpful yet shallow summaries, and shifted from initial scepticism to cautious optimism about its ethical use in nursing.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003e\u0026nbsp;Higashitsuji et.al. (2025)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eClarify the effectiveness of ChatGPT in case-based learning by using this application for case creation,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eSingle-group pre-post design and a blinded nonrandomized\u003cbr\u003e\u0026nbsp;crossover design.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003e\u0026nbsp;n= (17); n=(8) faculty members and n=(9) students; \u0026nbsp;evaluated faculty-created cases and ChatGPT-generated cases, with nursing students discussing the cases in recorded groups, analyzed through surveys and transcripts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eUsing ChatGPT to create case studies.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eEducators must still critically address concerns about data privacy, ethical issues, and the accuracy of generated content.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eCase creation time differed significantly, with 106 min and 71 min for manual and. ChatGPT, respectively (95% CI = 1.0\u0026ndash;1,299.6, p = 0.042). There were no significant differences in the perceived burden of creation and discussion quality.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eJhallad et.al., (2024)\u003c/p\u003e\n \u003cp\u003ePalestine and Jordan\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo determine the factors that affect the usefulness and sustainability of artificial intelligence tools used in nursing education.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eDescriptive Cross-Sectional Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (204) undergraduate nursing students A Questionnaire utilising the Technological Acceptance Model (TAM), the Information System Success Model (ISSM), and the Online Learning Self-Efficacy (OLSE) was used.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eOutlines general applications of Gen AI in nursing education through virtual simulations, personalised learning resources, remote clinical practice, and immersive VR/AR environments that improve clinical skills, decision-making, and teamwork.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eStudents\u0026apos; learning achievements were the top factor influencing AI tool use, with perceived enjoyment and intention to use also highly rated. Information quality and system quality were the most important factors, and mobile apps were the most widely used AI tools, followed by ChatGPT, PowerPoint AI, VR, and simulation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eKapadia, et.al. (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo evaluate the impact of LLM-generated dialogues on the training and development of nursing students.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eComparative Case Study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003eUsing lexical and semantic similarity tests compared to clinical expert-generated scripts, assessed the potential of LLMs as suitable alternatives for script generation.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eThe application of LLMs within VR environments to simulate patient-nurse dialogues.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eUnderstanding, adapting models to healthcare contexts, and the implications of AI use in patient care scenarios.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eGPT-3.5 excels in Health History scenarios with good alignment to human evaluation, but shows gaps in doctor portrayal during Bedside Conversation simulations, indicating a need for improvement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eKara\u0026ccedil;ay \u0026amp; Yaşar (2025)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo reveal sophomore nursing students\u0026apos; experiences and perspectives toward ChatGPT\u003cbr\u003e\u0026nbsp;activities used as a teaching tool in the classroom.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eDescriptive qualitative design\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003e\u0026nbsp;n= (24) sophomore nursing students who participated in ChatGPT activities in the classroom responded to a researcher-developed survey.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003e2 hours of theory and 2 hours of lab on abdominal and neurological exams included a student activity where participants summarized the lectures using ChatGPT-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eChatGPT classroom activities increased participants\u0026apos; knowledge, critical thinking, and satisfaction, facilitating group work and access to information, while highlighting the need to evaluate AI responses critically.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eKong, et. al., (2024)\u003c/p\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo understand nursing students\u0026apos; experience of using artificial intelligence for learning.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQualitative study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en (14) undergraduate nursing students shared their experiences and feelings with AI during semi-structured interviews, offering suggestions for course content, with data analysed thematically.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eChatbots built (ChatGPT, OpenAI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eOver-reliance on AI, responsible use respecting laws and values, and educators\u0026apos; role in guiding responsible AI use and digital literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eStudents felt a mix of confusion, excitement, and motivation with AI, developing confidence, teamwork, and benefiting from teacher support, while showing interest in further learning and improved teaching methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eKowitlawakul, et.al. (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSingapore, Thailand, USA, \u0026amp; Ireland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo evaluate the usability and perceptions of a newly developed AI-TAS (Teaching Assistant System) for a leadership and management module.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003ePilot Field Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (21); undergraduate nursing students, demographic data form and usability questionnaires, followed by participating in a focus group interview.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eArtificial Intelligence- Teaching Assistant System (AI-TAS): Conversations on topics like conflict management, change, time management, and problem-solving, using the chatbot to inquire about course content and promote interaction.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eReliance on AI may lack depth and understanding, with technical issues and limited content affecting engagement, highlighting the need for human oversight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eParticipants valued AI-TAS for revision and support, but suggested improvements in usability, features, engagement, and the integration of quizzes, gamification, and human tutoring to enhance effectiveness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eLane, et. al., (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eThe use of AI in nursing education presents a guide to Gen AI implementation in nursing education.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eCase-Based Descriptive Study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (95) Nurse Educators completed a SWOT analysis on implementing Gen AI, analysed thematically using Kiger and Varpio\u0026rsquo;s (2020) method.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eChatGPT assist with administrative and educational tasks like summarising research, creating syllabi, drafting letters, reviewing papers, developing exam questions, designing case studies, and crafting assessment rubrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eUnclear guidelines, conflicts with nursing values, academic dishonesty, AI biases, and the need for responsible AI literacy and ethics in education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003e\u0026nbsp;AI tools like ChatGPT offer benefits such as time-saving, improved simulation, and promoting critical thinking, but also pose risks including inaccuracies, hindering creativity, ethical concerns, and potentially replacing faculty roles.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eLebo \u0026amp; Brown (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo examine the application of AI patient case simulations in nursing education.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eAn exploratory case study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en=(50); undergraduate nursing students; simulation performance was objectively assessed with a computer-generated score based on assessment thoroughness, patient interviews, and completed questions during the simulation.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eAI patient cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eBias in AI simulations may occur if scenarios lack diversity, and inaccuracies in avatar responses or features could lead to misconceptions or inadequate practice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eStudents generally responded positively, valuing the simulation for practising dialogues and medication review, but some found asking complex questions challenging and wanted more assessment options due to AI limitations in simulating physical findings.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eLiaw, et. al. (2023)\u003c/p\u003e\n \u003cp\u003eUSA, Singapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo evaluate nursing students\u0026rsquo; competencies and experiences in communicating with an AI medical doctor.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eA Mixed-Methods Design\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (32) undergraduate nursing students completed pre- and post-tests on communication skills and self-efficacy, used surveys to evaluate experiences with AI-enabled VRS, and 5 participated in focus groups for deeper insights.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eThe development of a novel AI-enabled virtual reality simulation (AI-enabled VRS).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eCheating, plagiarism, and AI hallucinations, but features like data control aim to mitigate privacy risks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eNursing students significantly improved in communication skills and self-efficacy after using the VRS, found the AI agent helpful, and viewed the simulation as acceptable and useful, recommending it as a complement to, not a replacement for, face-to-face training.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eMakhlouf et.al., (2024)\u003c/p\u003e\n \u003cp\u003eEgypt and Saudi Arabia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003e\u0026nbsp;To evaluate the effectiveness of designing a knowledge-based artificial intelligence chatbot system for a nursing training program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQuasi-experimental design\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003e\u0026nbsp;n= (73) nurses: the study used multiple validated instruments to assess nurses\u0026rsquo; demographics, AI knowledge, perceptions of AI in nursing care, opinions on AI use, and views on nursing chatbots.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eA six-month study developed and integrated a chatbot into training, evaluating its effectiveness through surveys on nurses\u0026apos; knowledge, perceptions, and skills across specialities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eAI chatbot improved nurses\u0026apos; knowledge, perceptions, and attitudes, particularly in patient safety and care efficiency. Nurses viewed chatbots as valuable and user-friendly tools to support safe, evidence-based nursing and enhance patient outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eMiao \u0026amp; Ahn\u003c/p\u003e\n \u003cp\u003e, (2024)\u003c/p\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo assess the performance of ChatGPT-4 on multiple-choice and open-ended questions derived from nursing examinations in the Chinese context.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eAn Exploratory Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003eThe data sets of the Chinese National Nursing Licensure Examination spanning 2021 to 2023 were used to evaluate the accuracy of GPT-4 in multiple-choice questions. The performance of GPT-4 on open-ended questions was examined using 18 case-based questions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eEvaluate GPT-4\u0026apos;s performance on nursing exam questions in China, covering multiple-choice and open-ended items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eClear guidelines are essential for the ethical use of technology by students, along with vigilance to prevent educational disparities and social biases.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eChat GPT-4 achieved 71% accuracy on nursing questions over three years, excelling in calculations but limited in social, ethical, psychological, and image-based queries, with moderate performance in open-ended responses and challenges in specificity, prioritisation, and current standards.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eMolu (2025)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTurkey\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eCompare the effectiveness of an AI-based care plan learning strategy versus standard training on nursing students\u0026apos; newborn resuscitation outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eA Quasi-Experimental Study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en=(72) nursing students; n= (36) \u0026nbsp; \u0026nbsp; experimental group, which received care plans based on AI, n= (36) control group, which received traditional instruction with pre- and post-tests assessing neonatal resuscitation knowledge and student information.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eUsing Chat GPT to create an AI-based care plan learning strategy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eChatGPT lacks clinical expertise, real-time data access, and the latest guidelines, requiring careful use and further research in AI care plans.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eThe AI-based care plan group showed significantly better learning outcomes in newborn resuscitation post-test, with students expressing positive views and recognising AI\u0026apos;s benefits for nursing.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eParker, et. al., (2023)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eExplores the suitability of ChatGPT for automated writing evaluation in writing instruction.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQuasi-Experimental Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (42) \u0026nbsp;undergraduate nursing students: n=(18); first year undergraduate nursing students, \u0026nbsp; n= (24) third-year graduate nursing students: 42 texts were analysed.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eEach text was entered separately into ChatGPT-3 following an input prompt that included a rubric with constructs of language proficiency intended to represent aspects of complexity, accuracy, and fluency (CAF), commonly used as criteria for assessing writing development.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eConcerns include student cheating, plagiarism, and AI\u0026apos;s tendency to generate inaccurate or unjustified information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eChatGPT graded more strictly, offering detailed, human-like feedback focused on macro-level writing aspects and suggesting improvements, demonstrating its usefulness as an automated writing evaluation (AWE) tool.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eReed \u0026amp; Dodson (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo determine how the use of Gen AI patient backstories as a pre-simulation strategy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQualitative Cross-Sectional Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (15) junior-level BSN students, using a researcher-developed survey. \u0026nbsp;Content analysis was completed following the simulation.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eThe use of AI to create patient stories for simulations.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eConcerns over unethical student use and plagiarism hinder the integration of technology.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eAI pre-simulation generation enhances simulation prep by reducing anxiety, improving knowledge, and strengthening emotional connection with patient stories.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eReeder \u0026amp; Lee (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo understand what assignments need to be revised or\u003cbr\u003e\u0026nbsp;replaced if Gen AI could produce automated answers sufficient to pass a given course\u003cbr\u003e\u0026nbsp;assignment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eMixed Methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003eChatGPT\u0026apos;s ability to generate passing responses in two online graduate courses, in Nursing Informatics and Data Science, using a minimal user approach and evaluating its replies against course rubrics in 12 key discussions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eTo \u0026quot;proof\u0026quot; assignments against inappropriate use of Gen AI tools (ChatGPT).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eCourse policies should be revised to acknowledge appropriate AI use, and individual meetings should assess students\u0026apos; understanding and application of AI tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eChatGPT failed to produce passing responses in Nursing Informatics discussions but could pass three of four assignments in Data Science, with students reporting AI use in professional practice for tasks like technical documentation and qualitative analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eSaat\u0026ccedil;i, et.al., (2024)\u003c/p\u003e\n \u003cp\u003eTurkey\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo examine the effect of nursing students\u0026rsquo; use of artificial intelligence (AI) tools while preparing patient education materials on the understandability, actionability and quality of content material.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eRandomised Controlled Trial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (180) nursing students were divided into a control group (89) using traditional resources and an intervention group (91) using AI tools plus traditional resources; their patient education materials were evaluated using PEMAT (Patient Education Materials Assessment Tool) and the Global Quality Scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eUsing Gen AI tools to create patient education leaflets, ChatGPT, Copilot and Gemini.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eStudents using AI tools like ChatGPT, Copilot, and Gemini showed significantly higher PEMAT scores in understandability, actionability, and quality, indicating AI integration enhances educational content effectiveness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eSaban \u0026amp; Dubovi (2024)\u003c/p\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eExplores the potential of the Gen AI tool (ChatGPT) as clinical support for nurses and nursing students.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eCross-Sectional Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (30) Emergency room registered nurses (i.e. experts); n=\u0026thinsp;(38) nursing students. Evaluated clinical decision-making across scenarios, comparing responses and performance metrics such as time and length using questionnaires and ChatGPT responses.\u003c/p\u003e\n \u003cp\u003eTop of Form\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eComparing ChatGPT responses to clinical scenarios to those of nurses on different levels of experience using the AI model, OpenAI, ChatGPT, large language models LLM, and OpenAI Interface.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eEnsure cautious and transparent use and responsibly integrate it into education and practice to safeguard patient safety, prevent bias, and maintain clinical judgment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eChatGPT\u0026apos;s triage performance was comparable to that of experts and novices, but it tended to over-triage and suggest unnecessary tests. However, it provided faster response times and more detailed, uncertainty-indicating responses that sometimes included additional diagnostic information.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eShin et.al., (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eKorea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo evaluate the effects of artificial\u003cbr\u003e\u0026nbsp;Intelligence-assisted learning on nursing students\u0026apos; ethical decision-making and clinical reasoning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eA Quasi-Experimental Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en=(99) nursing students: n= (52) experimental group, n = (47) control group. Using student reports, two evaluators independently graded the scores based on two criteria (ethical standards and nursing processes) for the two groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eBoth groups reviewed a child abuse case involving a 4-year-old with health issues, with the experimental group using AI ChatGPT 3.5 and the control group using textbooks to explore ethical and professional considerations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eNursing curricula should include ethical guidance on AI, focusing on legal considerations, responsible data management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eThe control group outperformed the experimental group in terms of ethical understanding and critical thinking, utilised a wider range of resources, and demonstrated greater confidence in their learning. In contrast, the experimental group relied more heavily on textbooks and expressed concerns about the reliability of AI.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eSimsek-Cetinkaya, et. al., (2023)\u003c/p\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo evaluate the effectiveness of AI-assisted screen-based simulation in teaching breast self-examination skills in nursing undergraduate students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eComparative intervention trial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (103) first-year nursing students: split into AI-assisted screen-based simulation (52) and standard patient simulation (51) groups; data were collected via student forms, breast examination checklist, satisfaction and confidence scales, and anxiety inventories.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eAI-powered virtual patient simulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eThe standard patient simulation group scored highest in breast self-examination skills, with significant differences between groups. The AI-assisted screen-based simulation group had higher anxiety and satisfaction levels, both with significant differences.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eTseng et.al., (2025)\u003c/p\u003e\n \u003cp\u003eTaiwan\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eThe effectiveness of AI literacy and the application of ChatGPT and Copilot in academic nursing report writing.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eExploratory Case Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (203); Senior nursing students compared AI-enhanced teaching using ChatGPT and Copilot with traditional methods in a course on case report writing, using the ADDIE model and scaffolding, measuring AI literacy with MAILS, and evaluating performance through standards-based reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eChat GPT; Large language models such as ChatGPT, Copilot, and BERT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eResponsible AI use, accountability, supervision, and maintaining professional standards as key ethical considerations in nursing education and practice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eHigh reliability of the MAILS questionnaire, with significant improvements in AI literacy, creation, self-efficacy, and self-competency after an 18-week AI intervention. Students extensively utilised AI tools like ChatGPT and Copilot, and the experimental group scored higher in assessments, demonstrating strong consistency and positive correlations between self-efficacy, literacy, and competency.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eVaughn et al. (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo evaluate the effectiveness of using\u003cbr\u003e\u0026nbsp;ChatGPT as a tool in simulation design.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003ePost-test survey design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en (18) reviewers, 3 Adult Health Nursing subject matter experts, 9 simulation design experts, and 6 sim-ops speciality experts reviewed the ChatGPT-generated simulation scenarios and completed the survey.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eThe study describes how ChatGPT generated five simulation scenarios in less than 15 seconds each, and gave a title and listed scenario objectives for each one that were realistic based on the subject matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eCareful review, inclusive language, and caution are needed, emphasising the importance of critical evaluation and expert input to avoid inaccuracies in educational content.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eData analysis showed that scenarios differed in realism and completeness, with some missing pertinent details but accurate information overall; reviewers generally responded positively, often surprised by the richness of content in each scenario\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eWhite, et. al., (2024)\u003c/p\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eTo determine if a three-phased educational intervention significantly improved nursing students\u0026rsquo; attitudes toward older adults.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eA pre-/post-test study design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en = (151) senior-level community health\u003cbr\u003e\u0026nbsp;nursing course students participating in an AI in Education event. The study used the UCLA Geriatrics Attitudes Survey to assess changes in nursing students\u0026rsquo; attitudes toward older adults.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003eAI-driven, virtual, non-immersive simulation experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eDid not outline any ethical considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eResults showed a slight increase in scores after the intervention (mean = 35.07 vs. 34.50), with a small effect size (d = 0.15). The improvement was marginally significant at the 0.10 level (t = 1.88, p = 0.06).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eWolf (2023)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eEvaluating the impact of an Automated Essay Scoring assessment on MSc Nursing student writing skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eQuasi-Experimental design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003en= (64) MSc nursing students: Pre-and post-test survey data on student self-efficacy and task value, and automated essay scoring (AES) of writing proficiency were measured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003e\u0026nbsp;An AI-powered writing assessment, IntelliMetric\u0026reg;, the AI system was trained to recognise patterns in an effective persuasive essay and calculate scores based on the faculty\u0026rsquo;s scoring of hundreds of essays responding to the same prompt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eSupporting diverse students, and using automated assessments, emphasising the efficiency, reliability, and validity of the data produced.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eThe course significantly boosted students\u0026rsquo; confidence and writing skills, improving various proficiency areas, while their perceived value of scholarly writing remained high and unchanged.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eWu et.al., (2024)\u003c/p\u003e\n \u003cp\u003eChina\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.24%;\"\u003e\n \u003cp\u003eEvaluates LLMs\u0026apos; ability to answer NCLEX-RN and NNLE questions in multiple languages, assessing their potential as multilingual nursing education tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1184%;\"\u003e\n \u003cp\u003eCross-sectional study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3305%;\"\u003e\n \u003cp\u003eThe study tested ChatGPT 4.0, ChatGPT 3.5, and Bard on original and translated NCLEX-RN and NNLE questions, comparing their accuracy across languages and models.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.254%;\"\u003e\n \u003cp\u003elarge language models (LLMs) ChatGPT, for preparing for NCLEX exams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2088%;\"\u003e\n \u003cp\u003eLinguistic bias, privacy, informed consent, fairness, accountability, and the need for clear regulations to ensure the responsible and equitable application of this technology.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.7298%;\"\u003e\n \u003cp\u003eChatGPT 4.0 outperformed ChatGPT 3.5 and Bard in answering NCLEX-RN and NNLE questions across languages, achieving higher accuracy in both English and Chinese, with better performance on original English questions.\u003c/p\u003e\n \u003cp\u003eTop of Form\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Educators and Students\u0026apos; Attitudes to Gen AI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eAuthor, Year of Publication\u003c/p\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eStudy Aim(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eResearch Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003eMethodology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eEthical Considerations\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eResults\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eAbdel-Moat et.al. (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEgypt\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eThis study aimed to assess nursing interns\u0026rsquo; perception of artificial intelligence applications in nursing.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eDescriptive design\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (425) nurse interns. Self-developed AI technology questionnaire and a personal and work-related form were utilised during clinical shifts over a two-month period (August to September 2023).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eThe loss of human touch affects patient satisfaction, data privacy and security issues, and the risk of algorithmic bias leading to unequal treatment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eOverall, interns have low awareness (34.8%) of AI\u0026apos;s role in nursing, with slightly more positive attitudes (34.9%) than concerns (34.5%). Many are uncertain or fearful (45.5%) about AI replacing their roles, while only 13.8% are optimistic about its potential, indicating a gap in AI education and integration in nursing training.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eAhmed et.al., (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUAE, Egypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eInvestigate the experiences of nursing students in the UAE regarding the barriers and opportunities associated with the use of ChatGPT.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eDescriptive qualitative phenomenological design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (27) nursing students\u0026apos; experiences with ChatGPT at the University of Sharjah, UAE, using qualitative interviews to uncover both barriers and opportunities in its use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eConcerns over ChatGPT output, limited capability and security concerns. Lack of up-to-date information, privacy-related issues (unauthorised data sharing, fraud, other forms of misuse).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eStudents recognise ChatGPT\u0026apos;s advantages, such as saving time, providing 24/7 access, and supporting personalised learning. However, concerns about privacy, cognitive impact, reliance, and information reliability influence their acceptance, highlighting the need for ethical guidelines and digital literacy education to ensure responsible use.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eAlenazi, (2025)\u003c/p\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eInvestigate factors influencing nursing students\u0026rsquo; acceptance and use of AI.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (213) nursing students examined how the Unified Theory of Acceptance and Use of Technology (UTAUT) factors, Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, affect their intention to use and actual use of AI.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eBriefly addresses privacy and data security concerns.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003ePerceived usefulness strongly influences behavioural intention and actual use among nursing students, with behavioural intention mediating this relationship; effort expectancy, social influence, and facilitating conditions had less impact, and the model explained moderate to weak variance in intention and use.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eBouriami, et. al., (2025)\u003c/p\u003e\n \u003cp\u003eMorocco\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eTo examine nurse educators\u0026apos; knowledge, attitudes, and perceptions of ChatGPT, as well as their experience of using ChatGPT in active teaching methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional pilot study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (104) nurse educators. Used a self-developed questionnaire based on key concepts identified in a literature review.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eRisks of plagiarism, cheating, and the need for clear policies and training to promote responsible and ethical use among educators and students.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eOver half, 55.76% of respondents, saw ChatGPT as a tool that enables student cheating, while the majority, 70.52% were indifferent to its use. Additionally, 40.77% avoided using it and opposed student use due to concerns about inaccuracy and the potential for misinformation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eEl-Sayed et.al., (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSaudi Arabia, Egypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eExplores how AI literacy influences the relationship between an innovation mindset and nursing students\u0026apos; confidence in their career and talent skills (CTSE).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (596) nursing students. Correlation and regression analyses to explore the relationship between AI literacy and innovative thinking, as measured by the Innovative Thinking Competencies Scale (ITCS). The Scale for the Assessment of Nonexperts\u0026rsquo; AI Literacy (SNAIL), The Career and Talent Development Self-Efficacy Scale (CTD-SES)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eDoes not explicitly raise specific ethical concerns; it emphasises the importance of ethical awareness and responsible integration of AI in nursing education and practice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eNursing students had moderate levels of AI literacy, innovation mindset and career and talent self-efficacy. \u0026nbsp;AI literacy significantly moderates the relationship between nursing students\u0026rsquo; innovation mindset and their career and talent self-efficacy, making it more positive.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eEl-Sayed et.al., (2025)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEgypt and Saudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eTo examine the moderating effect of AI literacy on the associations between an innovation mindset and nursing students\u0026rsquo; career and talent self-efficacy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional research design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (596) nursing students; The Innovative Thinking Competencies Scale (ITCS) \u0026nbsp;to assess nursing students\u0026rsquo; competencies in innovative thinking. The Scale for the Assessment of Nonexperts\u0026rsquo; AI Literacy (SNAIL) and The Career and Talent Development Self-Efficacy Scale (CTDSES) to assess nursing students\u0026rsquo; perceptions of their CTSE.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eThere is a vital need to teach responsible AI use and ethics in healthcare to help nurses navigate AI ethically while prioritising patient care, highlighting the importance of policies, guidelines, mentorship, reflective practices, and specialised AI ethics courses.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eStudy revealed that nursing students had moderate levels of AI literacy, innovation mindset and career and talent self-efficacy. Nursing students\u0026rsquo; career and talent self-efficacy were significantly predicted by their innovative mindset and AI literacy. AI literacy significantly moderates the relationship between nursing students\u0026rsquo; innovation mindset and their career and talent self-efficacy, making it more positive.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eKilci Erciyas et.al., (2024)\u003c/p\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eExplore the connection between individual innovativeness levels and attitudes toward artificial intelligence among nursing and midwifery students.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional, descriptive, and exploratory correlational design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (500) nursing students: The Individual Innovativeness Scale (IIS), and the General Attitudes toward Artificial Intelligence Scale (GAAIS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eDoes not explicitly raise specific ethical considerations.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eMost students (94%) did not receive AI or innovation education, and over 97% did not participate in innovation research, resulting in generally low innovativeness levels, which were linked to more favourable attitudes toward AI. Students with AI training, innovative engagement, or ideas scored higher on innovativeness and positive perceptions, with the IIS significantly predicting attitudes\u0026mdash;higher innovativeness correlated with more positive views of AI.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eGunawan, et. al., (2024b)\u003c/p\u003e\n \u003cp\u003eThailand, Indonesia, Hong Kong, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eThis study aimed to explore the perspectives of Indonesian nursing students on the utilisation of ChatGPT in their learning\u003cbr\u003e\u0026nbsp;process.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eQualitative Descriptive Design\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (29) second-year Indonesian nursing students. Focus Group Discussion (Deductive Thematic Analysis)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eVerifying AI-generated information, preventing plagiarism, and ethically integrating AI tools like ChatGPT into curricula highlights the need for nursing educators to teach responsible use, information validation, and maintain academic integrity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eChatGPT can be helpful but requires verification due to potential inaccuracies; core nursing skills like empathy and judgment cannot be replaced by AI, serving as tools for enhancement; and there is an urgent need to modernise curricula to incorporate AI, address ethical concerns, and better prepare students for technological advancements.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eHashish \u0026amp; Alnajjar, (2024)\u003c/p\u003e\n \u003cp\u003eEgypt and Saudia Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eThis study aimed to assess the perceived knowledge, attitudes, and skills of nursing students regarding digital transformation, as well as their digital health literacy (DHL) and attitudes toward AI. Furthermore, we investigated the potential correlations among these variables.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional research design\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (266) nursing students a structured questionnaire consisting of six sections was used, covering personal information, knowledge, skills and attitudes toward digital transformation, digital skills, DHL, and attitudes toward AI. Descriptive statistics and Pearson correlation were employed for data analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eEthical considerations encompass data privacy, security, the responsible use of digital and AI tools, equitable access, and the need for guidelines to ensure the ethical deployment of technology in nursing education and practice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eStudents\u0026apos; acceptance of AI is influenced by their attitudes and varies with their level of digital device use.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eIssa et.al., (2024)\u003c/p\u003e\n \u003cp\u003eJordan, the UAE, the Kingdom of Saudi Arabia (KSA), and Egypt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eThe study examined students\u0026apos; knowledge of AI, training, attitudes toward integrating AI in health professions education (HPE), and their perceptions of barriers to implementation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (642) \u0026nbsp;nursing students (n=218), medicine (n=173), nutrition (n=129), and physiotherapy (n=122). A self-developed, validated online questionnaire to assess students\u0026rsquo; AI knowledge, attitudes, and perceived barriers in health professions education (HPE).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eStudents should be guided on ethical considerations like patient privacy and confidentiality to build trust in AI in healthcare, with HPE programs emphasizing the development of ethical principles for handling big data to ensure responsible AI use.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eParticipants showed limited prior AI knowledge, with 66.4% reporting low training levels, though familiarity with AI concepts varied across countries, especially higher in the UAE and Egypt. Most students accessed AI knowledge via social media and online resources. Overall attitudes toward AI integration in health education were positive, with over half (51.2%) holding high attitudes, particularly in Egypt and the UAE.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eKang, et. al., (2023)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eKorea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eIdentify nursing students\u0026apos; awareness of using chatbots and factors influencing their usage intention.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eDescriptive design using a self-reported questionnaire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (289) nursing students self-reported questionnaires, both online via a Naver Form and offline. Adapted version of the Extended Technology Acceptance Model (ETAM).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eWhile not explicitly addressing ethics, the discussion implies considerations like chatbot reliability, professionalism, maintaining trust, and concerns about empathy and user trust in AI.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eParticipants had moderate awareness and perceptions of chatbots\u0026apos; usefulness, ease of use, and intention to use, with awareness influenced by satisfaction, educational effectiveness, and interest. Perceived value was the strongest predictor, accounting for 60.2% of the intention to use.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eLabrague et.al., (2023)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUSA and the Philippines\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eAimed to examine student nurses\u0026apos; readiness to embrace AI technology, explore associated factors, and identify perceived barriers to accessing AI technology.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional study design\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (321) nursing students: Questionnaire\u0026nbsp;\u003cbr\u003e\u0026nbsp;perceptions of AI applications, barriers to AI access, and technological proficiency and understanding of AI.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eThere may be barriers to access AI, such as a lack of computer skills, limited knowledge and awareness of AI, inadequate access to resources and time constraints.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eWhile over 79% are aware of AI in healthcare, only 40.6% are aware of AI in nursing, with a generally good understanding. Perceived barriers, such as a lack of knowledge, skills, and time, were moderate, and readiness to adopt AI was also moderate. Higher self-rated tech skills, better AI understanding, and positive perceptions predict greater readiness, accounting for 15.3% of the variance, with knowledge gaps and limited skills identified as key barriers.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eLiu, et al., (2024)\u003c/p\u003e\n \u003cp\u003eChina \u0026amp; USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eTo investigate the awareness and use of ChatGPT among second-year undergraduate nursing students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional research design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (46) undergraduate nursing students. \u0026nbsp;A self-developed questionnaire examining awareness and use of ChatGPT.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eDid not include any ethical implications of implementing Gen AI as a teaching, learning, or assessment application.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003e97.8% (45 students) were aware of ChatGPT, with most having learned about it online. Among those aware, 23 students used ChatGPT, mostly infrequently (\u0026le;1 time per week), and one used it more frequently (2-3 times per week). Students primarily used ChatGPT to enhance their learning (16 students), complete homework (6 students), engage in conversation (5 students), and write essays (4 students).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eLuo et.al., (2023)\u003c/p\u003e\n \u003cp\u003eChina\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eAimed to investigate Chinese nursing students\u0026apos; AIQ and employability status, as well as their cognition and demand for the latest AI tool- ChatGPT.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eA cross-sectional survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (1788) students; AIQ\u0026ndash;Self-Regulation\u0026ndash;College Student Employability\u0026rdquo; questionnaire: Using correlation analysis and multiple hierarchical regression analysis, explored the relevant factors in the employability of nursing college students.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eDid not include any ethical implications of implementing Gen AI as a teaching, learning, or assessment application.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eMost students (81.3%) had not used ChatGPT, and 65.4% had never heard of it before, mainly due to a lack of access or knowledge. Despite limited usage, students generally had positive attitudes toward AI tools. Their moderate AI knowledge was linked to creativity, communication, learning, self-regulation, and better employability, with the lowest scores in data-related skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eMa, et. al., (2025)\u003c/p\u003e\n \u003cp\u003eChina \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eTo explore the perceptions of nursing students and their experiences of using large language models and identify the facilitators and barriers by applying the Theory of Planned Behaviour.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eQualitative descriptive design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (24) nursing students (8 undergraduates, 11 postgraduates, 5 doctoral candidates) from 13 Chinese medical universities, using semi-structured online interviews in Mandarin and directed content analysis to explore factors influencing Large Language Model use among nursing students, following COREQ guidelines.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eDid not include any ethical implications of implementing Gen AI as a teaching, learning, or assessment application.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eIdentified 10 themes based on the Theory of Planned Behaviour. Facilitators included perceived value, positive expectations, media influence, role models, and model design with free access. Barriers involved perceived caution, organisational pressure, geographic restrictions, and digital literacy gaps that hindered student adoption of AI.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eMigdadi et.al., (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eJordan, Saudi Arabia,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eExamine the correlation between AI ethical awareness, attitudes, anxiety, and intention to use AI technology among Jordanian nursing students.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eDescriptive, cross-sectional design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (140) five-part questionnaire, covering (1) Sociodemographic data, (2) AI Ethical\u003cbr\u003e\u0026nbsp;Awareness, (3) Attitudes Toward AI, (4) AI Anxiety, and (5) Intention-to-Use AI Technology {Test for AI Ethical Awareness (TAIEA)}\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eTeaching and addressing ethical issues, such as privacy, fairness, and robot rights, are essential to promoting responsible development, use, and integration of AI in healthcare and education.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eMost student nurses have used AI and possess good knowledge; however, their ethical awareness, positive attitudes, and intent to adopt AI are low, despite experiencing low anxiety. Greater AI awareness is linked to more positive perceptions and willingness to use AI, emphasising the need for improved education to boost understanding and acceptance in nursing.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eMoskavich \u0026amp; Rozani, (2025)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIsrael\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eTo investigate the perceptions of health profession students regarding the use of ChatGPT and its potential impact on healthcare and education.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eMixed-methods approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (217) of undergraduate health profession students with 73 (33.6%) nursing students, 65 (30.0%) medical students, and 79 (36.4%) occupational therapy, physiotherapy, and speech therapy students. The study employed a self-developed structured questionnaire.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eStudents will outsource work to ChatGPT, and there is a need to promote responsible and ethical use of the platform in educational settings. Risks of academic dishonesty, privacy issues, the potential to undermine critical thinking skills, and the need for responsible and guided integration of ChatGPT.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eMost students (86.2%) were familiar with ChatGPT and viewed it positively, citing benefits like improved information access and innovative learning, though concerns about ethics, critical thinking, and verification were also raised. Perceptions were similar across health-related disciplines.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eSumengen, et al., (2025)\u003c/p\u003e\n \u003cp\u003eUSA \u0026amp; Turkey\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eTo evaluate the attitudes of nursing students and literacy levels in relation to AI, to understand how prepared nursing students are with AI in clinical practice.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eA descriptive, correlational, and cross-sectional research design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (366) undergraduate nursing students. The Artificial Intelligence Literacy Scale (AILS) and the General Attitudes Towards Artificial Intelligence Scale (GAAIS).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eStudents are still confused about AI\u0026apos;s ethical aspects, emphasising the lack of research on AI ethics in nursing and underscoring the need for comprehensive education on ethical issues associated with AI to ensure responsible and appropriate use in healthcare.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eAI literacy and positive attitudes toward AI increase with education, usage, academic level, and financial stability among nursing students, highlighting the importance of targeted AI education in nursing curricula to foster favourable perceptions and understanding.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eYalcinkaya et al., (2024)\u003c/p\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eTo determine nursing students\u0026apos; attitudes towards and readiness for AI.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eCross-sectional descriptive research design\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en= (291) at a nursing faculty in the west of Turkey, collected using the Individual Information Form, the General Attitudes towards Artificial Intelligence Scale (GAAIS), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eDoes not explicitly outline any ethical concerns related to Gen AI in nursing education, but states that ethical considerations should be addressed in the curriculum.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eStudents with positive attitudes toward AI, higher computer skills, and interest in AI applications tend to have greater readiness and more favourable perceptions of AI integration in nursing education. Subscale relationships showed positive associations between positive attitudes and cognition, ability, vision, and ethics. In contrast, negative attitudes had a weak negative correlation with cognition and a weak positive correlation with ethics.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eYang, (2024)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eKorea\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eIdentify factors in artificial intelligence ethics awareness among nursing students.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eDescriptive Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (140) nursing students; A self-administered questionnaire, 18 items (Digital literacy), 36 items (Moral sensitivity), 24 items (AI ethics awareness).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eThe focus on AI ethics awareness and education underscores the ethical need for responsible AI development and proper training of healthcare professionals to handle moral dilemmas and build trust.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eMost participants (75%) used AI devices, with high rates of ethics education (92.9%) and AI training (67.9%). AI ethics awareness was positively linked to digital literacy and moral sensitivity, with moral sensitivity being the primary factor, accounting for 14% of the variation and associated with greater AI ethics awareness among nursing students.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1828%;\"\u003e\n \u003cp\u003eYigit \u0026amp; Acikgoz, (2024)\u003c/p\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2258%;\"\u003e\n \u003cp\u003eTo evaluate the knowledge, attitude and anxiety\u003cbr\u003e\u0026nbsp;levels of future nurses about artificial intelligence applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1505%;\"\u003e\n \u003cp\u003eDescriptive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3548%;\"\u003e\n \u003cp\u003en = (552) nursing students; \u0026lsquo;Artificial Intelligence Anxiety Scale\u0026rsquo; (AIAS).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2903%;\"\u003e\n \u003cp\u003eWhile not explicitly outlining ethical considerations, the discussion highlights concerns such as confidentiality, data security, and moral responsibilities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.7957%;\"\u003e\n \u003cp\u003eThe average AI ethics awareness score was 51.68, with no significant link to demographics. Despite lacking formal training, students demonstrated a strong interest in AI education and viewed AI as beneficial for workload reduction and patient follow-up. However, they also expressed concerns about legal, ethical, privacy, empathy, employment, and security issues.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Generative Artificial Intelligence (Gen AI), Nursing Education, AI Education, Educational Technology, Scoping Review","lastPublishedDoi":"10.21203/rs.3.rs-7537351/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7537351/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGenerative Artificial Intelligence (Gen AI) is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more, based on inputs or prompts. Gen AI technologies are being increasingly integrated into healthcare education, including the field of nursing, where they are utilised to support a range of pedagogical activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003eThis scoping review examined and described the application of Gen AI as a teaching, learning and assessment strategy in Nursing education and examined the ethical implications of and attitudes towards its implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a scoping review using Arksey and O’Malley’s 5-step framework, as well as the PRISMA-ScR framework guidelines, and searched five databases: EMBASE (Elsevier), Web of Science Core (Clarivate), CINAHL \u0026amp; Medline (EBSCO), Applied Social Science Index and Abstracts, and ERIC (ProQuest). A wide search of grey literature was also conducted. Literature published in English between January 1st 2014, and July 1st 2025 was included in the review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOf the 1,251 articles retrieved, we identified 103 articles for inclusion in the review. There were 44 discussion/opinion/conference papers and 59 empirical research papers. Gen AI has predominantly been used for content creation simulation, personalised learning, tutoring, skill development and assessment. Students and Educators describe mixed attitudes towards the implementation of Gen AI, with several ethical concerns regarding the application of Gen AI in nursing education evident, including privacy, transparency, bias, and accountability issues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWhile there is growing openness to Gen AI, a body of work remains regarding ethical and educational challenges. Recommendations for educational practice and curriculum development include a need for clear policies and guidelines to ensure the ethical use of Gen AI resources by educators and students. Further research is needed to understand long-term effects and promote responsible implementation within the context of nursing education.\u003c/p\u003e","manuscriptTitle":"Applications, Attitudes and Ethical Considerations of Generative Artificial Intelligence (Gen AI) In Nursing Education: A Scoping Review.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 06:50:22","doi":"10.21203/rs.3.rs-7537351/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-12T04:09:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T16:35:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211662784985170627458345816967020374750","date":"2025-10-22T14:24:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66167617670585339760911841427912671563","date":"2025-10-22T01:23:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40885107090085797390067491332766615864","date":"2025-10-21T19:22:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T14:55:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183684360916069504647422285404909178860","date":"2025-09-16T02:07:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128170695034060199392231100916823125596","date":"2025-09-15T13:46:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T13:15:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-10T14:31:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T10:33:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-10T10:32:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-09-04T14:43:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1bc20d66-b1db-4113-a479-9c757cf8bc51","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T17:05:59+00:00","versionOfRecord":{"articleIdentity":"rs-7537351","link":"https://doi.org/10.1186/s12912-025-04253-9","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2026-01-16 16:30:22","publishedOnDateReadable":"January 16th, 2026"},"versionCreatedAt":"2025-09-23 06:50:22","video":"","vorDoi":"10.1186/s12912-025-04253-9","vorDoiUrl":"https://doi.org/10.1186/s12912-025-04253-9","workflowStages":[]},"version":"v1","identity":"rs-7537351","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7537351","identity":"rs-7537351","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}