Cognitive Status of Nursing Postgraduates Toward Generative Artificial Intelligence: A Qualitative Study Based on the UTAUT Framework

Cognitive Status of Nursing Postgraduates Toward Generative Artificial Intelligence: A Qualitative Study Based on the UTAUT Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cognitive Status of Nursing Postgraduates Toward Generative Artificial Intelligence: A Qualitative Study Based on the UTAUT Framework Ziyan Wang, Hongzhan Jiang, Meiqi Meng, Bo Li, Sihan Chen, Dan Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7632348/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in BMC Nursing → Version 1 posted 10 You are reading this latest preprint version Abstract Background Generative Artificial Intelligence has the potential to enhance research efficiency and reduce clinical workload for nursing postgraduates, gradually transforming the development of the healthcare and nursing sectors. Understanding nursing postgraduates' experiences and perceptions of Generative Artificial Intelligence tools is essential for promoting their proper application. Aim To comprehensively explore Chinese nursing postgraduates' perceptions, attitudes, and needs regarding GenAI using qualitative interviews. Design A qualitative study design. Methods Semi-structured interviews was conducted among 16 nursing postgraduates. Purposeful sampling selected master's degree nursing students with experience in the use of artificial intelligence. Thematic analysis was performed to identify recurring patterns and codes. Results Five major themes emerged from the analysis: performance expectancy, effort expectancy, social influence, usage attitudes and behaviors, and boundaries to Generative Artificial Intelligence adoption. The findings revealed nursing postgraduates’ generally positive perceptions and usage behaviors toward Generative Artificial Intelligence among participants, alongside their barriers and concerns in its application. Conclusions Generative Artificial Intelligence is increasingly integrated into research and practice in healthcare and nursing. Nursing students should approach Generative Artificial Intelligence tools rationally and apply them appropriately. This study demonstrates that nursing postgraduates hold a relatively positive attitude and cognitive stance toward Generative Artificial Intelligence, which differs significantly from that of undergraduate students. In light of the current lack of Generative Artificial Intelligence-related education, the study also proposes educational strategies tailored to the Chinese context. Generative Artificial Intelligence Nursing Postgraduates Nursing Education Qualitative Research 1. Introduction Since the official release of the generative artificial intelligence system ChatGPT in November 2022 [ 1 ], Generative Artificial Intelligence (GenAI) has rapidly attracted attention and sparked discussions across academic disciplines due to its outstanding natural language processing capabilities and content generation functions [ 2 ]. This technology has brought new opportunities for the development of nursing research, education, and clinical practice. GenAI can enhance the efficiency of nursing research, provide realistic patient scenarios for practice, and create personalized, interactive learning experiences. In clinical nursing practice, it can help reduce nurses’ workloads, improve patient care [ 3 – 5 ], and assist in constructing personalized nursing models [ 6 , 7 ]. GenAI is gradually being integrated into all aspects of the nursing field, becoming a significant force driving the advancement of the nursing profession. Therefore, as future research leaders and clinical backbones in nursing, postgraduate nursing students must not only possess solid professional knowledge but also master and effectively utilize transformative technologies such as GenAI. However, current research on nursing students' understanding of GenAI primarily focuses on undergraduate students, and their use of GenAI is often limited to course-related learning. For example, in the study conducted by ShinHi et al. [ 8 ], undergraduate nursing students perceived the advantages of GenAI mainly in helping them understand classroom knowledge and generate related practice questions. Studies have shown that nursing postgraduates differ significantly from undergraduates in terms of knowledge structure, clinical experience, research needs, and future professional roles [ 9 ]. So, only relying on data from undergraduate students cannot fully capture the true experiences, deeper perspectives, and specific considerations or obstacles that nursing postgraduates may have regarding GenAI. Qualitative research emphasizes understanding human experiences in specific contexts, with data derived from in-depth insights into phenomena. It is particularly suitable for exploring individuals’ deeper cognitive processes regarding certain events or technologies [ 10 ]. Therefore, in order to comprehensively and thoroughly explore nursing postgraduates' current understanding of GenAI, including their experiences in application, value judgments, potential concerns, and developmental needs, a qualitative study specifically targeting this group is necessary. The Unified Theory of Acceptance and Use of Technology (UTAUT) [ 11 ] is a widely adopted theoretical framework used to explain and predict healthcare providers’ acceptance and usage behaviors regarding new health information technologies. It integrates eight previous models, including the Technology Acceptance Model (TAM), TAM2, the Theory of Reasoned Action, and the Diffusion of Innovation Theory. The UTAUT describes four key constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy refers to the degree to which an individual believes that using the system will help improve job performance. Effort expectancy refers to the perceived ease of use. Social influence is defined as the degree to which an individual perceives pressure from people around them to use the system. Facilitating conditions refer to the extent to which an individual believes that the organizational and technical infrastructure supports system usage [ 11 , 12 ]. In the UTAUT model, behavioral intention to use a technology is determined by performance expectancy, effort expectancy, and social influence. Actual use is determined by behavioral intention and facilitating conditions [ 11 , 12 ]. The model also considers moderating variables such as gender, age, experience, and voluntariness of use, making it well-suited for systematically uncovering the underlying mechanisms of nursing postgraduates' cognition and usage behavior toward GenAI [ 11 , 12 ]. Therefore, this study, grounded in the UTAUT framework, employs semi-structured interviews and thematic analysis to explore Chinese nursing postgraduates’ perceptions, attitudes, and needs regarding GenAI from the perspectives of technological application, risk awareness, and future expectations. The findings aim to provide empirical evidence to support the application of GenAI in the nursing field and the development of an educational model for GenAI that aligns with the characteristics of the nursing discipline in China. 2. Methods 2.1 Study Design A qualitative study design was adopted, and qualitative data were collected through semi-structured interviews. The research process adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines[ 13 ]. 2.2 Participants From February to March 2025, full-time postgraduate nursing students from four medical universities in Beijing and Guangzhou, China, were selected using purposive sampling to participate in semi-structured interviews. Inclusion criteria included: (1) Full-time postgraduate nursing students who had completed at least one semester of coursework; (2) Experience with and prior use of GenAI tools; (3) Willingness to participate in the study. Exclusion criteria included: (1) Students on leave of absence, withdrawal, exchange programs, or other special academic statuses; (2) Those who had participated in similar studies previously. Sample size was determined based on data saturation—interviews continued until no new themes emerged [ 10 ]. 2.3 Data Collection Based on the UTAUT framework, a preliminary interview guide was developed and refined after two pilot interviews. The problems were designed as follows: (1) The performance expectancy dimension focused on how GenAI helps improve students’ learning and work capabilities (e.g., “Do you think GenAI helps with your nursing knowledge learning, such as theoretical understanding or memory? In what ways?”). (2) The effort expectancy dimension explored the perceived ease of using GenAI (e.g., “What GenAI functions do you find easiest to use? What difficulties or challenges have you encountered?”). (3) The usage behavior and intention dimension examined students’ willingness to use GenAI (e.g., “Do you think nursing students should actively use GenAI?”). (4) The social influence and facilitating conditions dimension focused on peer and environmental factors (e.g., “Have your classmates’ views on GenAI influenced your own attitude? If so, how?”). The full interview guide is available in Additional file 1 . Interviews were conducted using this guide in a semi-structured format. All interviewers received systematic training in qualitative methods, including mock interview practice. Participants were screened strictly according to the inclusion/exclusion criteria. Before each interview, the purpose of the study was explained and written informed consent was obtained. The one-on-one online interviews were facilitated using online video conferencing software to ensure clear audio and no interference. Each session lasted 20–30 minutes and was fully audio-recorded. Interviewers followed the guide closely, focusing on core research questions, listening attentively, and encouraging open expression of thoughts and feelings. Leading questions were avoided, and a neutral stance was maintained throughout. Each interview concluded once no new information emerged. 2.4 Data Analysis Within 24 hours after each interview, two researchers transcribed the recordings verbatim. The transcripts were then returned to participants for member checking and verification. To protect participant privacy, all identifying information was anonymized, with each participant was assigned a unique code (N1–N16). A thematic analysis approach [ 14 ] was applied, using the UTAUT framework as the analytical foundation. Four predetermined themes— performance expectancy, effort expectancy, social influence, usage attitudes and behaviors —guided the initial coding. NVivo 12.0 software was used to code transcripts line by line, categorizing data units under the corresponding themes. Building on the deductive coding, researchers read each data unit closely, identifying shared features and differences from a bottom-up perspective to develop subthemes. Emerging themes, such as ethical concerns, were identified with openness and incorporated into the final framework. The authors met several times to discuss the codes and identify the sub-themes and themes. 3.5 Ethical Considerations This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Beijing University of Chinese Medicine (Approval No. 2025BZYLL0103). Written informed consent was obtained from all participants prior to data collection. Measures were taken to ensure anonymity and confidentiality of participant data. All interview recordings and transcripts were stored on a password-protected personal computer, accessible only to the research team. 3. Results By the 12th interview, the emergence of new themes had significantly declined. The subsequent 4 interviews yielded no new core themes, indicating data saturation. In total, 16 postgraduate nursing students were included in the study, with a male-to-female ratio of 1:15 and their ages ranging from 21 to 26. Other general information of participants is presented in Table 1 . Table 1 General Information of the Research Subjects ID Year of Study Completed Clinical Practice Duration (months) N1 First-year Master's 10 N2 First-year Master's 10 N3 First-year Master's 10 N4 First-year Master's 10 N5 First-year Master's 10 N6 First-year Master's 10 N7 Second-year Master's 14 N8 Second-year Master's 14 N9 Second-year Master's 16 N10 Second-year Master's 14 N11 Second-year Master's 12 N12 Third-year Master's 22 N13 Third-year Master's 22 N14 Third-year Master's 16 N15 Third-year Master's 22 N16 Third-year Master's 16 Using thematic analysis, five major themes and seventeen subthemes were identified, as summarized in Table 2 . Table 2 Summary of Interview Topics Theme Sub-Theme Performance Expectancy Reduction of clinical nursing workload Improvement of nursing education quality Assistance in academic writing and learning comprehension Effort Expectancy Convenient, efficient, and user-friendly Need for critical evaluation of generated content Social Influence Keeping pace with technological development Significant peer influence Strong demand for training High cost of educational implementation Usage Attitudes and Behaviors Strong motivation and willingness to recommend Rational perspective on use Varying degrees of tech anxiety Widespread application Boundaries to GenAI Adoption Information security risks Unclear ethical boundaries Challenges to academic integrity 3.1 Performance Expectancy 3.1.1 Reduction of Clinical Nursing Workload Participants believed that GenAI could assist nurses in adapting to hospital information systems, aid in data processing, and support clinical decision-making—ultimately improving work efficiency and reducing nursing workload. N14: “Being familiar with AI tools can improve my work efficiency and help me adapt better to hospital information systems.” N16 “ Patients can get answers from AI, which reduces the burden on nurses. ” 3.1.2 Improvement of Nursing Education Quality GenAI was viewed as a catalyst for innovative teaching models (e.g., simulation-based learning) and an efficient means to optimize teaching resources. N11: “GenAI is very helpful for realistic simulation teaching. Introducing it into nursing education would definitely be beneficial.” N16: “Proper classroom integration could improve both teaching quality and student learning efficiency.” 3.1.3 Assistance in Academic Writing and Learning Comprehension Participants used GenAI to support academic writing tasks such as literature summarization, identifying innovative points, grammar polishing, and translation. It also helped clarify learning content. N8: “For my research topic, I ask GenAI to give me an overview, then I look for gaps to explore.” N3: “I input several papers and let it to summarize the key points, which saves me time and effort.” 3.2 Effort Expectancy 3.2.1 Convenient, Efficient, and User-Friendly Almost all participants attributed GenAI’s popularity in research to its easy-to-use and efficient nature. N2: “GenAI helps me quickly find cutting-edge theories in nursing.” N12: “It’s very popular and super convenient to use.” 3.2.2 Need for Critical Evaluation of Generated Content Participants noted challenges such as difficulty verifying content accuracy, irrelevant results, and ineffective prompts. N1: “We must judge the accuracy ourselves, but we can’t always be sure it’s right.” N12: “The visuals generated weren’t good, and the output was often low-quality or off-topic.” 3.3 Social Influence 3.3.1 Keeping Pace with Technological Development Participants generally viewed GenAI as a necessary trend in the big data era, requiring proactive learning. N12: “You must adapt, or you’ll be left behind.” N13: “We should embrace AI and actively learn how to use it.” 3.3.2 Strong Peer Influence Many began using GenAI after peer recommendations, showing how peer attitudes affected personal adoption. N1: “I started after my classmates recommended it.” N9: “If someone says a tool is bad, I might stop using it and try what they suggest instead.” 3.3.3 Strong Demand for Training All participants lacked formal training and expressed a desire for structured instruction. N7: “It should be part of the curriculum, with step-by-step guidance and materials.” N9: “I don’t think I’m using it well—I really want a course on it.” 3.3.4 High Cost of Educational Implementation Participants noted that applying GenAI in classroom teaching would require significant financial investment. N9: “AI is expensive. Widespread classroom use may not be feasible.” N14: “Ordinary institutions may not have the funding to support it.” 3.4 Usage Attitudes and Behaviors 3.4.1 Strong Motivation and Willingness to Recommend Motivations included time-saving, limitations of traditional tools, and peer influence. Many had positive experiences and recommended GenAI to others. N11: “It saves a lot of time and energy.” N3: “My classmate recommended it. It seemed useful, so I decided to give it a try.” 3.4.2 Rational Perspective on Use Participants maintained a critical view of GenAI, stressing the need for caution and responsible use. N1: “You have to assess whether the output is truly accurate.” N12: “It helps generate ideas, but might make us lazy if we rely too much on it.” 3.4.3 Varying Degrees of Tech Anxiety Some participants felt anxious about being left behind or replaced by AI; others were more composed, viewing AI as a tool, not a threat. N16: “If others are using these tools and you’re not, it can feel very stressful.” N1: “AI feels like it might replace human work entirely.” N12: “AI lacks human empathy—nurses also need to care for patients emotionally. That can’t be replaced.” 3.4.4 Widespread Application GenAI usage was common and frequent among participants. N1: “I use it almost every day.” N7: “Lots of people around me, not just in nursing, use it regularly.” 3.5 Boundaries to GenAI Adoption 3.5.1 Information Security Risks Most participants worried about data privacy, especially regarding sensitive academic content. N15: “I’m afraid of data being stolen, especially when entering paper content—it might get leaked.” 3.5.2 Unclear Ethical Boundaries Some raised concerns about privacy and ethical issues in GenAI use, especially involving patient information. N11: “I’m not sure if using GenAI with patient data violates privacy.” 3.5.3 Challenges to Academic Integrity Participants feared that reliance on GenAI in research might lead to plagiarism or data fabrication. N5: “If an article is written entirely with AI, it could harm originality and lead to misconduct.” 4. Discussion As GenAI gradually integrates into the nursing field, nursing students’ cognition and application of GenAI is of great importance. This study conducted in-depth interviews with 16 nursing graduate students from China, analyzing the findings based on the UTAUT framework. The results show that nursing graduate students’ understanding of GenAI can be categorized into five major themes: performance expectancy, effort expectancy, social influence, usage attitudes and behaviors, and boundaries to GenAI adoption. These findings not only validate the applicability of the UTAUT model in analyzing GenAI acceptance among nursing graduate students but also reveal their overall positive attitude and cognitive diversity, as well as highlight the current lack of instructional support for GenAI use in nursing education. 4.1 The Current Acceptance of GenAI Among Nursing Graduate Students and the Applicability of UTAUT The study found that Chinese nursing graduate students generally hold positive performance expectations for GenAI, recognizing its value in reducing clinical workload, improving nursing education quality, and assisting academic writing and knowledge comprehension. Meanwhile, the convenience and efficiency of GenAI as an expected goal, as well as the macro pressure of social environment factors such as the development of the times and the usage recommendations from peers, have had a positive impact on the attitude towards using GenAI. However, challenges such as difficulties in evaluating generated content, technical anxiety, data security concerns, ethical ambiguity, and academic integrity risks hinder deeper adoption. Regarding usage attitudes and behaviors, nursing graduate students exhibit a pragmatic and rational stance: while many express strong motivation and willingness to recommend GenAI as a practical tool to improve efficiency, they also emphasize the importance of critical evaluation, caution against overreliance, and call for systematic training to overcome obstacles such as command inaccuracy. This rational perspective echoes findings from qualitative research by Han et al. [ 8 ] and cross-sectional studies like George et al. [ 15 ]. Notably, this study identifies a “dual-motivation mechanism” in the acceptance of GenAI. Positive motivation stems from the perceived value of GenAI in teaching and clinical support, its convenience, and peer influence, as well as the perception of GenAI as an inevitable trend in digital healthcare. Negative motivation, as expressed by participant N16 (“Failure to adapt to GenAI may result in being left behind by society”), highlights technological adaptation anxiety as a reverse driver. This aligns with the "voluntariness" variable in the UTAUT model [ 11 ]: when positive motivation dominates, perceived usefulness plays a greater role in shaping intention; when negative motivation dominates, social influence becomes more significant under a sense of compulsion. These findings strongly support the applicability of the UTAUT model in exploring GenAI acceptance among Chinese nursing graduate students. They also highlight the unique cognitive traits within the nursing discipline—such as heightened ethical sensitivity and differing levels of technical anxiety—which suggest the need to adapt UTAUT to specific professional contexts. This study provides solid empirical foundations for targeted interventions, including the development of GenAI training programs, resource optimization strategies, and clearer ethical guidelines. 4.2 Differences in GenAI Perceptions Among Student Groups This study shows that nursing graduate students hold a generally positive view of GenAI, particularly its potential to improve research and work efficiency. These findings are consistent with domestic and international research on nursing students’ perceptions of GenAI [ 8 , 16 – 19 ], collectively demonstrating its practical value in nursing education and practice. This shared optimism also indicates nursing students’ strong willingness to accept and adapt to new technologies. Although GenAI has many advantages in assisting learning and nursing practice, there are also many problems and obstacles surrounding its application. For example, studies by Summers et al. [ 20 ] have pointed out that doubts about data accuracy, risks of academic integrity, and tendencies toward over-reliance are the core concerns commonly shared by students. Labrague et al. [ 16 ] have shown that lack of knowledge hinders the use of GenAI technology, while Han et al. [ 8 ] have identified cost barriers. These challenges are also evident in this study, indicating that Chinese nursing students face similar issues as their international counterparts. Addressing these problems requires strategies that promote GenAI literacy, encourage critical assessment, ensure ethical use, and provide institutional financial support [ 8 ]. Compared to undergraduates, nursing graduate students tend to have deeper and more forward-looking perspectives on GenAI[ 9 ]. In this study, the respondents not only focused on the nursing learning scenarios, but also offered certain insights into the application of GenAI in nursing clinical practice, such as for clinical decision support and patient consultation, which confirms the comprehensiveness of the graduate students' understanding of GenAI. These cognitive differences may be attributed to variations in academic experience and clinical exposure. Master's students have more advanced theoretical training and research experience, which may lead to a deeper understanding of GenAI’s technical logic and interdisciplinary potential. Additionally, their richer clinical experience allows them to better identify both the benefits and limitations of GenAI in real-world practice. Furthermore, levels of anxiety about GenAI varied among graduate students. Some feared it could replace nursing jobs, expressing significant concern, while others acknowledged its usefulness but doubted its ability to fully replace human care due to deficiencies in empathy and complex judgment. These conflicting views underscore the cognitive diversity within the group and offer meaningful perspectives for the nursing field to rationally navigate technological change. 4.3 Urgent Need for Teaching Guidance Studies have shown that students with a better understanding of GenAI are more likely to recognize its potential and less likely to fear its negative effects [ 16 ]. This study found that nursing graduate students struggle to evaluate content accuracy, worry about dependency, and have difficulty understanding ethical boundaries—likely due to insufficient knowledge and limited technical skills. Similar barriers have been reported among undergraduates, where a lack of AI literacy hinders effective use [ 16 ]. Meanwhile, participants in this study also expressed strong demand for educational resources and called for GenAI-related courses to be introduced by their institutions, further highlighting the urgent need for formal instruction. To support proper and ethical use of GenAI, comprehensive educational guidance is essential. International scholars have proposed several GenAI training strategies for medical students. For example, Bisdas et al. [ 21 ] suggested replacing certain modules to avoid overloading students, while Mousavi et al. [ 22 ] recommended diverse approaches such as workshops, manuals, and step-by-step guides to facilitate practical learning. However, these methods may not be directly applicable in China due to differences in curricular design, resource allocation, and training objectives. Locally adapted GenAI education models are therefore needed. First, institutions can leverage China’s post-COVID "online + offline" hybrid teaching approach [ 23 ] to develop structured online GenAI courses that include video lectures, case analyses, and simulations, complemented by offline Q&A and hands-on sessions. This would ensure learning quality while reducing in-person workload. Second, aligned with the "Healthy China Strategy," which prioritizes public well-being, Chinese medical schools must enhance education in medical ethics to serve societal needs [ 24 ]. As GenAI penetrates deeper into healthcare, ethical issues such as data privacy, algorithm bias, and liability become increasingly urgent. Ethical education should be embedded into GenAI training programs. For example, an embedded AI ethics framework [ 25 ] could integrate technical application with safety guidelines, forming a comprehensive curriculum. National-level policy should also promote standardization of AI ethics education [ 26 ] to ensure quality and alignment across institutions. 4.4 Study Limitations This study has several limitations. First, it only included master’s-level nursing students, excluding doctoral students. This was due to easier access and lower sampling difficulty among master's students. However, given that doctoral students may differ in their research focus, technical skills, and familiarity with GenAI, the current findings represent only a preliminary exploration and may not fully reflect the entire postgraduate nursing population. Future studies should target doctoral students or compare across education levels to identify moderating effects of academic training on GenAI acceptance. Second, all participants were from Beijing and Guangzhou, two technologically advanced cities with high levels of digital infrastructure and openness to innovation. These locations may not represent the broader national context. Therefore, generalizing the findings to other regions may introduce bias due to environmental differences. Broader sampling is needed to reflect the full diversity of GenAI perceptions among Chinese nursing students. 5. Conclusions GenAI is gradually becoming integrated into nursing research and practice, and nursing students should approach its use rationally and responsibly. Based on the UTAUT model, this study explored the current state of GenAI awareness among Chinese nursing graduate students, examining their perceptions, attitudes, and needs. The results confirm the strong applicability of UTAUT in analyzing GenAI acceptance, providing a comprehensive framework for evaluating students’ motivations, obstacles, and expectations. Nursing graduate students generally hold a positive outlook toward GenAI, and their acceptance is shaped by a dual-motivation mechanism—driven both by the technology’s functional attributes and by the developmental needs of the discipline. While similarities exist across nursing education levels, graduate students demonstrate deeper and more nuanced understandings compared to undergraduates. Finally, in light of the widespread lack of GenAI-related instruction, this study proposes an education model adapted to China’s context to support broader and more effective integration of GenAI into the nursing field. Abbreviations GenAI Generative Artificial Intelligence TAM Technology Acceptance Model UTAUT Unified Theory of Acceptance and Use of Technology Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Beijing University of Chinese Medicine (Approval No. 2025BZYLL0103) Consent for publication Not applicable. Availability of data and materials The datasets used and 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 work was supported by Traditional Chinese Medicine Innovation Team and Talent Support Program - National Traditional Chinese Medicine Multidisciplinary Cross-Innovation Team Project. Authors' contributions WZY and JHZ were responsible for formal analysis, investigation, methodology and writing (original draft, review and editing). HYF and LXJ were responsible for funding acquisition, project administration, supervision and writing (review and editing). MMQ was responsible for methodology and writing (review and editing). LB was responsible for resources and supervision. CSH and YD were responsible for formal analysis and investigation. Acknowledgements Not applicable. References AI O. Introducing ChatGPT.; 2022. Sandrone S. Medical education in the metaverse. NAT MED. 2022;28(12):2456–7. 10.1038/s41591-022-02038-0 . Tam W, Huynh T, Tang A, Luong S, Khatri Y, Zhou W. Nursing education in the age of artificial intelligence powered Chatbots (AI-Chatbots): Are we ready yet? NURS EDUC TODAY. 2023;129:105917. 10.1016/j.nedt.2023.105917 . Topaz M, Peltonen LM, Michalowski M, Stiglic G, Ronquillo C, Pruinelli L, Song J, O'Connor S, Miyagawa S, Fukahori H. 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10:51:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16429,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7632348/v1/71134c3d674781eb97700f5f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Status of Nursing Postgraduates Toward Generative Artificial Intelligence: A Qualitative Study Based on the UTAUT Framework","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince the official release of the generative artificial intelligence system ChatGPT in November 2022 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], Generative Artificial Intelligence (GenAI) has rapidly attracted attention and sparked discussions across academic disciplines due to its outstanding natural language processing capabilities and content generation functions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This technology has brought new opportunities for the development of nursing research, education, and clinical practice. GenAI can enhance the efficiency of nursing research, provide realistic patient scenarios for practice, and create personalized, interactive learning experiences. In clinical nursing practice, it can help reduce nurses\u0026rsquo; workloads, improve patient care [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and assist in constructing personalized nursing models [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenAI is gradually being integrated into all aspects of the nursing field, becoming a significant force driving the advancement of the nursing profession. Therefore, as future research leaders and clinical backbones in nursing, postgraduate nursing students must not only possess solid professional knowledge but also master and effectively utilize transformative technologies such as GenAI. However, current research on nursing students' understanding of GenAI primarily focuses on undergraduate students, and their use of GenAI is often limited to course-related learning. For example, in the study conducted by ShinHi et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], undergraduate nursing students perceived the advantages of GenAI mainly in helping them understand classroom knowledge and generate related practice questions. Studies have shown that nursing postgraduates differ significantly from undergraduates in terms of knowledge structure, clinical experience, research needs, and future professional roles [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. So, only relying on data from undergraduate students cannot fully capture the true experiences, deeper perspectives, and specific considerations or obstacles that nursing postgraduates may have regarding GenAI. Qualitative research emphasizes understanding human experiences in specific contexts, with data derived from in-depth insights into phenomena. It is particularly suitable for exploring individuals\u0026rsquo; deeper cognitive processes regarding certain events or technologies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, in order to comprehensively and thoroughly explore nursing postgraduates' current understanding of GenAI, including their experiences in application, value judgments, potential concerns, and developmental needs, a qualitative study specifically targeting this group is necessary.\u003c/p\u003e\u003cp\u003eThe Unified Theory of Acceptance and Use of Technology (UTAUT) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] is a widely adopted theoretical framework used to explain and predict healthcare providers\u0026rsquo; acceptance and usage behaviors regarding new health information technologies. It integrates eight previous models, including the Technology Acceptance Model (TAM), TAM2, the Theory of Reasoned Action, and the Diffusion of Innovation Theory. The UTAUT describes four key constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy refers to the degree to which an individual believes that using the system will help improve job performance. Effort expectancy refers to the perceived ease of use. Social influence is defined as the degree to which an individual perceives pressure from people around them to use the system. Facilitating conditions refer to the extent to which an individual believes that the organizational and technical infrastructure supports system usage [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the UTAUT model, behavioral intention to use a technology is determined by performance expectancy, effort expectancy, and social influence. Actual use is determined by behavioral intention and facilitating conditions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The model also considers moderating variables such as gender, age, experience, and voluntariness of use, making it well-suited for systematically uncovering the underlying mechanisms of nursing postgraduates' cognition and usage behavior toward GenAI [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, this study, grounded in the UTAUT framework, employs semi-structured interviews and thematic analysis to explore Chinese nursing postgraduates\u0026rsquo; perceptions, attitudes, and needs regarding GenAI from the perspectives of technological application, risk awareness, and future expectations. The findings aim to provide empirical evidence to support the application of GenAI in the nursing field and the development of an educational model for GenAI that aligns with the characteristics of the nursing discipline in China.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003eA qualitative study design was adopted, and qualitative data were collected through semi-structured interviews. The research process adhered to the \u003cem\u003eStandards for Reporting Qualitative Research (SRQR)\u003c/em\u003e guidelines[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Participants\u003c/h2\u003e\u003cp\u003e From February to March 2025, full-time postgraduate nursing students from four medical universities in Beijing and Guangzhou, China, were selected using purposive sampling to participate in semi-structured interviews.\u003c/p\u003e\u003cp\u003eInclusion criteria included: (1) Full-time postgraduate nursing students who had completed at least one semester of coursework; (2) Experience with and prior use of GenAI tools; (3) Willingness to participate in the study.\u003c/p\u003e\u003cp\u003eExclusion criteria included: (1) Students on leave of absence, withdrawal, exchange programs, or other special academic statuses; (2) Those who had participated in similar studies previously.\u003c/p\u003e\u003cp\u003eSample size was determined based on data saturation\u0026mdash;interviews continued until no new themes emerged [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Collection\u003c/h2\u003e\u003cp\u003eBased on the UTAUT framework, a preliminary interview guide was developed and refined after two pilot interviews. The problems were designed as follows: (1) The performance expectancy dimension focused on how GenAI helps improve students\u0026rsquo; learning and work capabilities (e.g., \u0026ldquo;Do you think GenAI helps with your nursing knowledge learning, such as theoretical understanding or memory? In what ways?\u0026rdquo;). (2) The effort expectancy dimension explored the perceived ease of using GenAI (e.g., \u0026ldquo;What GenAI functions do you find easiest to use? What difficulties or challenges have you encountered?\u0026rdquo;). (3) The usage behavior and intention dimension examined students\u0026rsquo; willingness to use GenAI (e.g., \u0026ldquo;Do you think nursing students should actively use GenAI?\u0026rdquo;). (4) The social influence and facilitating conditions dimension focused on peer and environmental factors (e.g., \u0026ldquo;Have your classmates\u0026rsquo; views on GenAI influenced your own attitude? If so, how?\u0026rdquo;). The full interview guide is available in \u003cb\u003eAdditional file 1\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eInterviews were conducted using this guide in a semi-structured format. All interviewers received systematic training in qualitative methods, including mock interview practice. Participants were screened strictly according to the inclusion/exclusion criteria. Before each interview, the purpose of the study was explained and written informed consent was obtained. The one-on-one online interviews were facilitated using online video conferencing software to ensure clear audio and no interference. Each session lasted 20\u0026ndash;30 minutes and was fully audio-recorded. Interviewers followed the guide closely, focusing on core research questions, listening attentively, and encouraging open expression of thoughts and feelings. Leading questions were avoided, and a neutral stance was maintained throughout. Each interview concluded once no new information emerged.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e\u003cp\u003eWithin 24 hours after each interview, two researchers transcribed the recordings verbatim. The transcripts were then returned to participants for member checking and verification. To protect participant privacy, all identifying information was anonymized, with each participant was assigned a unique code (N1\u0026ndash;N16).\u003c/p\u003e\u003cp\u003eA thematic analysis approach [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] was applied, using the UTAUT framework as the analytical foundation. Four predetermined themes\u0026mdash; performance expectancy, effort expectancy, social influence, usage attitudes and behaviors \u0026mdash;guided the initial coding. NVivo 12.0 software was used to code transcripts line by line, categorizing data units under the corresponding themes. Building on the deductive coding, researchers read each data unit closely, identifying shared features and differences from a bottom-up perspective to develop subthemes. Emerging themes, such as ethical concerns, were identified with openness and incorporated into the final framework. The authors met several times to discuss the codes and identify the sub-themes and themes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Ethical Considerations\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Beijing University of Chinese Medicine (Approval No. 2025BZYLL0103). Written informed consent was obtained from all participants prior to data collection. Measures were taken to ensure anonymity and confidentiality of participant data. All interview recordings and transcripts were stored on a password-protected personal computer, accessible only to the research team.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eBy the 12th interview, the emergence of new themes had significantly declined. The subsequent 4 interviews yielded no new core themes, indicating data saturation. In total, 16 postgraduate nursing students were included in the study, with a male-to-female ratio of 1:15 and their ages ranging from 21 to 26. Other general information of participants is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeneral Information of the Research Subjects\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear of Study\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCompleted Clinical Practice Duration (months)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird-year Master's\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing thematic analysis, five major themes and seventeen subthemes were identified, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Interview Topics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSub-Theme\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePerformance Expectancy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduction of clinical nursing workload\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImprovement of nursing education quality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssistance in academic writing and learning comprehension\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eEffort Expectancy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConvenient, efficient, and user-friendly\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeed for critical evaluation of generated content\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eSocial Influence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKeeping pace with technological development\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignificant peer influence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrong demand for training\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh cost of educational implementation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eUsage Attitudes and Behaviors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrong motivation and willingness to recommend\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRational perspective on use\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVarying degrees of tech anxiety\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidespread application\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eBoundaries to GenAI Adoption\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInformation security risks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnclear ethical boundaries\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChallenges to academic integrity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Performance Expectancy\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Reduction of Clinical Nursing Workload\u003c/h2\u003e\u003cp\u003eParticipants believed that GenAI could assist nurses in adapting to hospital information systems, aid in data processing, and support clinical decision-making\u0026mdash;ultimately improving work efficiency and reducing nursing workload.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN14: \u0026ldquo;Being familiar with AI tools can improve my work efficiency and help me adapt better to hospital information systems.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eN16\u003c/strong\u003e\u003cp\u003e\u0026ldquo;\u003cem\u003ePatients can get answers from AI, which reduces the burden on nurses.\u003c/em\u003e\u0026rdquo;\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Improvement of Nursing Education Quality\u003c/h2\u003e\u003cp\u003eGenAI was viewed as a catalyst for innovative teaching models (e.g., simulation-based learning) and an efficient means to optimize teaching resources.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN11: \u0026ldquo;GenAI is very helpful for realistic simulation teaching. Introducing it into nursing education would definitely be beneficial.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN16: \u0026ldquo;Proper classroom integration could improve both teaching quality and student learning efficiency.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Assistance in Academic Writing and Learning Comprehension\u003c/h2\u003e\u003cp\u003eParticipants used GenAI to support academic writing tasks such as literature summarization, identifying innovative points, grammar polishing, and translation. It also helped clarify learning content.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN8: \u0026ldquo;For my research topic, I ask GenAI to give me an overview, then I look for gaps to explore.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN3: \u0026ldquo;I input several papers and let it to summarize the key points, which saves me time and effort.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Effort Expectancy\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Convenient, Efficient, and User-Friendly\u003c/h2\u003e\u003cp\u003eAlmost all participants attributed GenAI\u0026rsquo;s popularity in research to its easy-to-use and efficient nature.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN2: \u0026ldquo;GenAI helps me quickly find cutting-edge theories in nursing.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN12: \u0026ldquo;It\u0026rsquo;s very popular and super convenient to use.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Need for Critical Evaluation of Generated Content\u003c/h2\u003e\u003cp\u003eParticipants noted challenges such as difficulty verifying content accuracy, irrelevant results, and ineffective prompts.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN1: \u0026ldquo;We must judge the accuracy ourselves, but we can\u0026rsquo;t always be sure it\u0026rsquo;s right.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN12: \u0026ldquo;The visuals generated weren\u0026rsquo;t good, and the output was often low-quality or off-topic.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Social Influence\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Keeping Pace with Technological Development\u003c/h2\u003e\u003cp\u003eParticipants generally viewed GenAI as a necessary trend in the big data era, requiring proactive learning.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN12: \u0026ldquo;You must adapt, or you\u0026rsquo;ll be left behind.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN13: \u0026ldquo;We should embrace AI and actively learn how to use it.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Strong Peer Influence\u003c/h2\u003e\u003cp\u003eMany began using GenAI after peer recommendations, showing how peer attitudes affected personal adoption.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN1: \u0026ldquo;I started after my classmates recommended it.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN9: \u0026ldquo;If someone says a tool is bad, I might stop using it and try what they suggest instead.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3 Strong Demand for Training\u003c/h2\u003e\u003cp\u003eAll participants lacked formal training and expressed a desire for structured instruction.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN7: \u0026ldquo;It should be part of the curriculum, with step-by-step guidance and materials.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN9: \u0026ldquo;I don\u0026rsquo;t think I\u0026rsquo;m using it well\u0026mdash;I really want a course on it.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.3.4 High Cost of Educational Implementation\u003c/h2\u003e\u003cp\u003eParticipants noted that applying GenAI in classroom teaching would require significant financial investment.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN9: \u0026ldquo;AI is expensive. Widespread classroom use may not be feasible.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN14: \u0026ldquo;Ordinary institutions may not have the funding to support it.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Usage Attitudes and Behaviors\u003c/h2\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1 Strong Motivation and Willingness to Recommend\u003c/h2\u003e\u003cp\u003eMotivations included time-saving, limitations of traditional tools, and peer influence. Many had positive experiences and recommended GenAI to others.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN11: \u0026ldquo;It saves a lot of time and energy.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN3: \u0026ldquo;My classmate recommended it. It seemed useful, so I decided to give it a try.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e3.4.2 Rational Perspective on Use\u003c/h2\u003e\u003cp\u003eParticipants maintained a critical view of GenAI, stressing the need for caution and responsible use.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN1: \u0026ldquo;You have to assess whether the output is truly accurate.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN12: \u0026ldquo;It helps generate ideas, but might make us lazy if we rely too much on it.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e3.4.3 Varying Degrees of Tech Anxiety\u003c/h2\u003e\u003cp\u003eSome participants felt anxious about being left behind or replaced by AI; others were more composed, viewing AI as a tool, not a threat.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN16: \u0026ldquo;If others are using these tools and you\u0026rsquo;re not, it can feel very stressful.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN1: \u0026ldquo;AI feels like it might replace human work entirely.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN12: \u0026ldquo;AI lacks human empathy\u0026mdash;nurses also need to care for patients emotionally. That can\u0026rsquo;t be replaced.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e3.4.4 Widespread Application\u003c/h2\u003e\u003cp\u003e GenAI usage was common and frequent among participants.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN1: \u0026ldquo;I use it almost every day.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eN7: \u0026ldquo;Lots of people around me, not just in nursing, use it regularly.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Boundaries to GenAI Adoption\u003c/h2\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003e3.5.1 Information Security Risks\u003c/h2\u003e\u003cp\u003eMost participants worried about data privacy, especially regarding sensitive academic content.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN15: \u0026ldquo;I\u0026rsquo;m afraid of data being stolen, especially when entering paper content\u0026mdash;it might get leaked.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003e3.5.2 Unclear Ethical Boundaries\u003c/h2\u003e\u003cp\u003eSome raised concerns about privacy and ethical issues in GenAI use, especially involving patient information.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN11: \u0026ldquo;I\u0026rsquo;m not sure if using GenAI with patient data violates privacy.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\u003ch2\u003e3.5.3 Challenges to Academic Integrity\u003c/h2\u003e\u003cp\u003eParticipants feared that reliance on GenAI in research might lead to plagiarism or data fabrication.\u003c/p\u003e\u003cp\u003e\u003cem\u003eN5: \u0026ldquo;If an article is written entirely with AI, it could harm originality and lead to misconduct.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAs GenAI gradually integrates into the nursing field, nursing students\u0026rsquo; cognition and application of GenAI is of great importance. This study conducted in-depth interviews with 16 nursing graduate students from China, analyzing the findings based on the UTAUT framework. The results show that nursing graduate students\u0026rsquo; understanding of GenAI can be categorized into five major themes: performance expectancy, effort expectancy, social influence, usage attitudes and behaviors, and boundaries to GenAI adoption. These findings not only validate the applicability of the UTAUT model in analyzing GenAI acceptance among nursing graduate students but also reveal their overall positive attitude and cognitive diversity, as well as highlight the current lack of instructional support for GenAI use in nursing education.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e4.1 The Current Acceptance of GenAI Among Nursing Graduate Students and the Applicability of UTAUT\u003c/h2\u003e\u003cp\u003eThe study found that Chinese nursing graduate students generally hold positive performance expectations for GenAI, recognizing its value in reducing clinical workload, improving nursing education quality, and assisting academic writing and knowledge comprehension. Meanwhile, the convenience and efficiency of GenAI as an expected goal, as well as the macro pressure of social environment factors such as the development of the times and the usage recommendations from peers, have had a positive impact on the attitude towards using GenAI. However, challenges such as difficulties in evaluating generated content, technical anxiety, data security concerns, ethical ambiguity, and academic integrity risks hinder deeper adoption. Regarding usage attitudes and behaviors, nursing graduate students exhibit a pragmatic and rational stance: while many express strong motivation and willingness to recommend GenAI as a practical tool to improve efficiency, they also emphasize the importance of critical evaluation, caution against overreliance, and call for systematic training to overcome obstacles such as command inaccuracy. This rational perspective echoes findings from qualitative research by Han et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and cross-sectional studies like George et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, this study identifies a \u0026ldquo;dual-motivation mechanism\u0026rdquo; in the acceptance of GenAI. Positive motivation stems from the perceived value of GenAI in teaching and clinical support, its convenience, and peer influence, as well as the perception of GenAI as an inevitable trend in digital healthcare. Negative motivation, as expressed by participant N16 (\u0026ldquo;Failure to adapt to GenAI may result in being left behind by society\u0026rdquo;), highlights technological adaptation anxiety as a reverse driver. This aligns with the \"voluntariness\" variable in the UTAUT model [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]: when positive motivation dominates, perceived usefulness plays a greater role in shaping intention; when negative motivation dominates, social influence becomes more significant under a sense of compulsion.\u003c/p\u003e\u003cp\u003eThese findings strongly support the applicability of the UTAUT model in exploring GenAI acceptance among Chinese nursing graduate students. They also highlight the unique cognitive traits within the nursing discipline\u0026mdash;such as heightened ethical sensitivity and differing levels of technical anxiety\u0026mdash;which suggest the need to adapt UTAUT to specific professional contexts. This study provides solid empirical foundations for targeted interventions, including the development of GenAI training programs, resource optimization strategies, and clearer ethical guidelines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Differences in GenAI Perceptions Among Student Groups\u003c/h2\u003e\u003cp\u003eThis study shows that nursing graduate students hold a generally positive view of GenAI, particularly its potential to improve research and work efficiency. These findings are consistent with domestic and international research on nursing students\u0026rsquo; perceptions of GenAI [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], collectively demonstrating its practical value in nursing education and practice. This shared optimism also indicates nursing students\u0026rsquo; strong willingness to accept and adapt to new technologies.\u003c/p\u003e\u003cp\u003eAlthough GenAI has many advantages in assisting learning and nursing practice, there are also many problems and obstacles surrounding its application. For example, studies by Summers et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] have pointed out that doubts about data accuracy, risks of academic integrity, and tendencies toward over-reliance are the core concerns commonly shared by students. Labrague et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] have shown that lack of knowledge hinders the use of GenAI technology, while Han et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] have identified cost barriers. These challenges are also evident in this study, indicating that Chinese nursing students face similar issues as their international counterparts. Addressing these problems requires strategies that promote GenAI literacy, encourage critical assessment, ensure ethical use, and provide institutional financial support [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCompared to undergraduates, nursing graduate students tend to have deeper and more forward-looking perspectives on GenAI[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In this study, the respondents not only focused on the nursing learning scenarios, but also offered certain insights into the application of GenAI in nursing clinical practice, such as for clinical decision support and patient consultation, which confirms the comprehensiveness of the graduate students' understanding of GenAI. These cognitive differences may be attributed to variations in academic experience and clinical exposure. Master's students have more advanced theoretical training and research experience, which may lead to a deeper understanding of GenAI\u0026rsquo;s technical logic and interdisciplinary potential. Additionally, their richer clinical experience allows them to better identify both the benefits and limitations of GenAI in real-world practice.\u003c/p\u003e\u003cp\u003eFurthermore, levels of anxiety about GenAI varied among graduate students. Some feared it could replace nursing jobs, expressing significant concern, while others acknowledged its usefulness but doubted its ability to fully replace human care due to deficiencies in empathy and complex judgment. These conflicting views underscore the cognitive diversity within the group and offer meaningful perspectives for the nursing field to rationally navigate technological change.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Urgent Need for Teaching Guidance\u003c/h2\u003e\u003cp\u003eStudies have shown that students with a better understanding of GenAI are more likely to recognize its potential and less likely to fear its negative effects [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This study found that nursing graduate students struggle to evaluate content accuracy, worry about dependency, and have difficulty understanding ethical boundaries\u0026mdash;likely due to insufficient knowledge and limited technical skills. Similar barriers have been reported among undergraduates, where a lack of AI literacy hinders effective use [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Meanwhile, participants in this study also expressed strong demand for educational resources and called for GenAI-related courses to be introduced by their institutions, further highlighting the urgent need for formal instruction. To support proper and ethical use of GenAI, comprehensive educational guidance is essential.\u003c/p\u003e\u003cp\u003eInternational scholars have proposed several GenAI training strategies for medical students. For example, Bisdas et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] suggested replacing certain modules to avoid overloading students, while Mousavi et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] recommended diverse approaches such as workshops, manuals, and step-by-step guides to facilitate practical learning. However, these methods may not be directly applicable in China due to differences in curricular design, resource allocation, and training objectives. Locally adapted GenAI education models are therefore needed.\u003c/p\u003e\u003cp\u003eFirst, institutions can leverage China\u0026rsquo;s post-COVID \"online\u0026thinsp;+\u0026thinsp;offline\" hybrid teaching approach [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to develop structured online GenAI courses that include video lectures, case analyses, and simulations, complemented by offline Q\u0026amp;A and hands-on sessions. This would ensure learning quality while reducing in-person workload. Second, aligned with the \"Healthy China Strategy,\" which prioritizes public well-being, Chinese medical schools must enhance education in medical ethics to serve societal needs [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. As GenAI penetrates deeper into healthcare, ethical issues such as data privacy, algorithm bias, and liability become increasingly urgent. Ethical education should be embedded into GenAI training programs. For example, an embedded AI ethics framework [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] could integrate technical application with safety guidelines, forming a comprehensive curriculum. National-level policy should also promote standardization of AI ethics education [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] to ensure quality and alignment across institutions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Study Limitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, it only included master\u0026rsquo;s-level nursing students, excluding doctoral students. This was due to easier access and lower sampling difficulty among master's students. However, given that doctoral students may differ in their research focus, technical skills, and familiarity with GenAI, the current findings represent only a preliminary exploration and may not fully reflect the entire postgraduate nursing population. Future studies should target doctoral students or compare across education levels to identify moderating effects of academic training on GenAI acceptance.\u003c/p\u003e\u003cp\u003eSecond, all participants were from Beijing and Guangzhou, two technologically advanced cities with high levels of digital infrastructure and openness to innovation. These locations may not represent the broader national context. Therefore, generalizing the findings to other regions may introduce bias due to environmental differences. Broader sampling is needed to reflect the full diversity of GenAI perceptions among Chinese nursing students.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eGenAI is gradually becoming integrated into nursing research and practice, and nursing students should approach its use rationally and responsibly. Based on the UTAUT model, this study explored the current state of GenAI awareness among Chinese nursing graduate students, examining their perceptions, attitudes, and needs.\u003c/p\u003e\u003cp\u003eThe results confirm the strong applicability of UTAUT in analyzing GenAI acceptance, providing a comprehensive framework for evaluating students\u0026rsquo; motivations, obstacles, and expectations. Nursing graduate students generally hold a positive outlook toward GenAI, and their acceptance is shaped by a dual-motivation mechanism\u0026mdash;driven both by the technology\u0026rsquo;s functional attributes and by the developmental needs of the discipline.\u003c/p\u003e\u003cp\u003eWhile similarities exist across nursing education levels, graduate students demonstrate deeper and more nuanced understandings compared to undergraduates. Finally, in light of the widespread lack of GenAI-related instruction, this study proposes an education model adapted to China\u0026rsquo;s context to support broader and more effective integration of GenAI into the nursing field.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGenAI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenerative Artificial Intelligence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTAM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTechnology Acceptance Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUTAUT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnified Theory of Acceptance and Use of Technology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u003c/strong\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Beijing University of Chinese Medicine (Approval No. 2025BZYLL0103)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Traditional Chinese Medicine Innovation Team and Talent Support Program - National Traditional Chinese Medicine Multidisciplinary Cross-Innovation Team Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWZY and JHZ were responsible for formal analysis, investigation, methodology and writing (original draft, review and editing). HYF and LXJ were responsible for funding acquisition, project administration, supervision and writing (review and editing). MMQ was responsible for methodology and writing (review and editing). LB was responsible for resources and supervision. CSH and YD were responsible for formal analysis and investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAI O. Introducing ChatGPT.; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSandrone S. Medical education in the metaverse. 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PERSPECT MED EDUC. 2023;12(1):399\u0026ndash;410. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5334/pme.954\u003c/span\u003e\u003cspan address=\"10.5334/pme.954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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, Nursing Postgraduates, Nursing Education, Qualitative Research","lastPublishedDoi":"10.21203/rs.3.rs-7632348/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7632348/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerative Artificial Intelligence has the potential to enhance research efficiency and reduce clinical workload for nursing postgraduates, gradually transforming the development of the healthcare and nursing sectors. Understanding nursing postgraduates' experiences and perceptions of Generative Artificial Intelligence tools is essential for promoting their proper application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo comprehensively explore Chinese nursing postgraduates' perceptions, attitudes, and needs regarding GenAI using qualitative interviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA qualitative study design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSemi-structured interviews was conducted among 16 nursing postgraduates. Purposeful sampling selected master's degree nursing students with experience in the use of artificial intelligence. Thematic analysis was performed to identify recurring patterns and codes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive major themes emerged from the analysis: performance expectancy, effort expectancy, social influence, usage attitudes and behaviors, and boundaries to Generative Artificial Intelligence adoption. The findings revealed nursing postgraduates’ generally positive perceptions and usage behaviors toward Generative Artificial Intelligence among participants, alongside their barriers and concerns in its application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerative Artificial Intelligence is increasingly integrated into research and practice in healthcare and nursing. Nursing students should approach Generative Artificial Intelligence tools rationally and apply them appropriately. This study demonstrates that nursing postgraduates hold a relatively positive attitude and cognitive stance toward Generative Artificial Intelligence, which differs significantly from that of undergraduate students. In light of the current lack of Generative Artificial Intelligence-related education, the study also proposes educational strategies tailored to the Chinese context.\u003c/p\u003e","manuscriptTitle":"Cognitive Status of Nursing Postgraduates Toward Generative Artificial Intelligence: A Qualitative Study Based on the UTAUT Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 10:51:26","doi":"10.21203/rs.3.rs-7632348/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T06:03:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T05:35:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T09:06:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337010327511813830350534241134972287680","date":"2025-10-31T11:25:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149451324479898691943114628975279033155","date":"2025-10-29T06:08:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T09:38:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-02T09:37:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-23T04:24:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-19T15:47:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-09-19T15:44:13+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":"a10e9ef2-2048-4820-a896-958594b66c10","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:03:18+00:00","versionOfRecord":{"articleIdentity":"rs-7632348","link":"https://doi.org/10.1186/s12912-025-04187-2","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2026-01-07 15:57:20","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2025-10-15 10:51:26","video":"","vorDoi":"10.1186/s12912-025-04187-2","vorDoiUrl":"https://doi.org/10.1186/s12912-025-04187-2","workflowStages":[]},"version":"v1","identity":"rs-7632348","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7632348","identity":"rs-7632348","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}