Faculty Perceptions of Artificial Intelligence Tools in Nursing Education in Saudi Arabia: Opportunities, Risks, and Readiness for Assessment Integration

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Abusabeib This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7729091/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Artificial intelligence (AI) is rapidly entering higher education; however, research regarding nursing faculty perceptions—especially concerning assessment—remains scarce in Saudi Arabia. Grasping the perceived opportunities, risks, and level of preparedness is crucial for the responsible implementation of these technologies. Objectives To explore nursing faculty members’ perceptions of AI opportunities and risks, as well as their readiness for integrating AI into assessments; to identify variations among groups based on training, rank, and experience; and to evaluate the construct validity of the measurement model. Methods A cross-sectional online survey was conducted among all faculty at a Saudi nursing college (N = 34; 85% response) during the academic year 2025–2026. Measures were adapted from the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), as well as published instruments on AI risks and readiness. Analyses included descriptive statistics, independent t -tests/ANOVA, Pearson correlations, and exploratory factor analysis, including an assessment of internal consistency. Results Faculty indicated a strong perception of opportunities (e.g., Performance Expectancy, M = 4.04, SD = 0.67) but expressed moderate concerns regarding risks, with the highest score being for Academic Integrity. Readiness showed a positive correlation with opportunities ( r = 0.64, p < 0.001) and with social influence/institutional support (both r = 0.69, p < 0.001), while it exhibited a negative correlation with risks ( r = − 0.28, p = 0.046). Faculty who received training perceived greater usefulness ( t = 3.01, p = 0.005) and higher readiness ( t = 2.82, p = 0.008); lecturers revealed greater integrity concerns than senior faculty members; faculty with over 15 years of experience reported a lower level of ease of use. The factor analysis confirmed a two-factor arrangement (opportunities, risks) with a high level of internal consistency. Conclusions Nursing faculty exhibit a positive attitude toward the use of AI in assessments but stress the importance of safeguards to ensure integrity and privacy. Readiness is influenced by the perceived advantages and the institutional environment, indicating the need for governance, approved tools, and focused professional development—particularly for faculty members with greater experience. Programmes should integrate redesigning assessments with clear policies and building capacity to promote responsible and equitable AI adoption. Multi-site, longitudinal studies assessing training and policy initiatives are recommended. artificial intelligence nursing education assessment faculty perceptions readiness Saudi Arabia Introduction Artificial Intelligence (AI) is revolutionising healthcare education by providing innovative resources that can enhance the processes of teaching, learning, and assessment [ 1 , 2 ]. In the realm of nursing education, applications of AI include virtual simulations, intelligent tutoring systems, as well as automated grading and feedback systems [ 1 , 3 ]. AI-driven tools have demonstrated potential in enhancing student engagement and academic success, providing personalised feedback, facilitating self-directed learning, and supporting educators in creating interactive and adaptive learning environments [ 4 ]. However, the incorporation of AI in educational contexts presents certain challenges. Faculty members are crucial to the adoption and effective use of AI tools; however, their perceptions, readiness, and concerns remain underexplored, particularly within the context of nursing education in Saudi Arabia [ 5 , 6 ]. Grasping these elements is vital for the effective integration of AI technologies into nursing curricula [ 1 ]. Recent research has indicated the possible advantages of AI in medical education, which include tailored learning experiences, enhanced accuracy in assessments, and improved efficiency in administrative duties [ 1 , 7 ]. For example, Rani et al. (2025) investigated the views of medical students and faculty regarding AI in medical education, highlighting its significance for enhancing curricula [ 1 ]. Likewise, Lee (2025) employed the Technology Acceptance Model (TAM) to analyse faculty acceptance of AI tools in medical education [ 8 ]. Despite such progress, apprehensions about the ethical repercussions of AI—such as bias, privacy concerns, and threats to academic integrity—continue to exist [ 9 ]. Salih et al. (2024) discovered that while faculty recognised the advantageous effects of AI on medical education, they also voiced concerns about its ethical dimensions [ 5 , 10 ]. These issues are particularly relevant in nursing education, where the humane aspects of care and ethical decision-making are fundamental themes in the curriculum [ 3 ]. In Saudi Arabia, the use of AI in nursing education is still in its nascent stages. Alshanberi et al. (2024) evaluated the knowledge and attitudes toward AI among faculty and students at a medical college in Saudi Arabia, underscoring the necessity for more research [ 5 ]. Comprehending faculty perceptions and readiness is critical for creating targeted interventions that address their concerns and encourage the successful integration of AI tools in nursing education [ 3 , 11 ]. This research focuses on filling the evidence gap regarding the perceptions of nursing faculty on AI for assessment in Saudi Arabia by (i) assessing perceived opportunities and risks, (ii) analysing readiness for AI implementation, (iii) evaluating differences among subgroups based on training, rank, and experience, and (iv) investigating construct validity (through exploratory factor analysis and reliability). Methodology Design This study employs a cross-sectional survey design and utilises Google Forms to collect data from faculty members at a nursing college in Saudi Arabia. The survey instrument is adapted from well-established models, including the TAM [12] and the UTAUT [13], to evaluate faculty perceptions regarding the use of AI tools in education. Additionally, Likert-scale items are implemented to measure perceived risks associated with AI, such as bias, privacy issues, and threats to academic integrity [2, 11]. Area and Setting The research was conducted at a nursing college in Saudi Arabia, providing context-specific insights into faculty perceptions of AI integration in nursing education. The environment comprises a diverse group of faculty members with varying levels of experience and familiarity with AI technologies. Variables The key variables include perceived opportunities (assessed through TAM and UTAUT constructs like Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and performance expectancy), perceived risks (evaluated through Likert-scale items concerning bias, privacy, and academic integrity), and faculty readiness (measured by self-reported willingness to adopt AI tools in assessment practices). Participants The focus population consists of faculty members at the nursing college who are involved in teaching and assessment. These faculty members directly engage with assessment practices and are likely to possess informed views on the integration of AI. During the 2025–2026 academic year, all faculty members working at the nursing college who were engaged in teaching and assessment and willing to participate were included in the study. Faculty on extended leave, those on sabbatical, those not participating in assessment activities, or those unwilling to be involved, were excluded. Data Collection Techniques Data collection was performed via an online survey using Google Forms. The questionnaire comprised four sections: (1) Demographics; (2) Opportunities and acceptance of AI, assessed through validated items from the TAM [12] and the UTAUT [13], (3) Risks and concerns, measured using items adapted from the Trust in AI in Healthcare Applications Scale [2] and the AI Ethical Concerns in Education Questionnaire [11]; and (4) Readiness, evaluated using the validated AI Readiness in Higher Education Scale [6]. Data processing and analysis A pilot test was conducted with 10 faculty members from a different health college within the same institution to assess the clarity, readability, and cultural appropriateness of the items. Normality (Shapiro–Wilk) and homogeneity of variances (Levene’s) were examined. Internal consistency was evaluated using Cronbach’s alpha. Construct validity was examined through an exploratory factor analysis prior to the main analysis. Data were analysed using SPSS version 26. Descriptives (mean, standard deviation, frequency) summarised sample characteristics and study variables. Differences between groups were examined through independent-samples t-tests or one-way ANOVA, with Holm-adjusted post-hoc analyses conducted when necessary; effect sizes are reported as Cohen’s d or partial η² along with 95% confidence intervals. The relationships among Opportunities, Risks, Readiness, Social Influence, and Institutional Support were explored using Pearson correlations. All negatively phrased (reversed) items were reverse-coded before analysis to guarantee consistency in their interpretation. Ethics approval and consent to participate Ethics approval was obtained from the Princess Nourah bint Abdulrahman University Institutional Review Board, Riyadh, Saudi Arabia (PNURSP2025R876). Electronic informed consent was obtained prior to participation. Participants have the right to withdraw at any time without facing any consequences. All data were securely stored and used exclusively for research purposes. The study adhered to the principles of the Declaration of Helsinki. Consent for publication Not applicable. This manuscript contains no individual person’s data (images, videos, or potentially identifying clinical details). Results Pilot Testing and Data Validation A pilot test was carried out involving 10 faculty members from a different health college within the same institution. Feedback was used to improve the wording while preserving the core concepts. The adequacy of the sample was favourable (KMO = 0.82), and the results of Bartlett’s test of sphericity were significant ( p < 0.001). Both parallel analysis and the scree plot indicated retention of two factors—Opportunities and Risks, which together accounted for 62.3% of the total variance. Each of the targeted items demonstrated loading of at least 0.60 on its respective factor, with no cross-loadings exceeding 0.30. The internal consistency of the subscales varied, with α values ranging from 0.78 to 0.87. Demographic Characteristics A total of 34 faculty members participated in the survey, resulting in a response rate of 85%. The average age was 41.9 years. The academic ranks included Lecturers (n = 9, 26.5%), Assistant Professors (n = 21, 61.8%), and Associate Professors (n = 4, 11.7%). Teaching experience varied from 3 to 25 years, with an average of 11.7 years. Approximately 75% of respondents reported prior use of AI tools in teaching or assessment, while 32.4% indicated that they had received formal training related to AI (Table 1). Table 1. Faculty Demographics (N = 34) Characteristic n (%) / Mean (SD) Age (years) 41.9 (7.9) Academic Rank – Lecturer 9 (26.5%) – Assistant Professor 21 (61.8%) – Associate Professor 4 (11.7%) Teaching Experience (years) 11.7 (5.8) Previous AI use in teaching 25 (73.5%) AI-related training received 11 (32.4%) Faculty Perceptions of AI Opportunities Faculty perceptions demonstrated a strong acknowledgement of AI’s potential. The scores for PU and PEOU were 3.93 (SD = 0.70) and 3.74 (SD = 0.69), respectively. The construct with the highest rating was Performance Expectancy (M = 4.04, SD = 0.67). Notably, Social Influence also received a high rating (M = 3.91, SD = 0.72), indicating substantial institutional backing for the integration of AI in education (Table 2). Table 2. Faculty Perceptions of AI Opportunities (N = 34) Construct Mean (SD) Perceived Usefulness (PU) 3.93 (0.70) Perceived Ease of Use (PEOU) 3.74 (0.69) Performance Expectancy 4.04 (0.67) Effort Expectancy 3.84 (0.71) Social Influence 3.91 (0.72) Facilitating Conditions 3.62 (0.74) Perceived Risks of AI in Assessment Faculty members showed moderate levels of concern regarding potential risks. As shown in Table 3, the most significant concern pertained to Academic Integrity (M = 3.85, SD = 0.72), followed by concerns about Bias & Fairness (M = 3.80, SD = 0.72). The apprehensions regarding Privacy and Accountability were somewhat lower. Table 3. Faculty Concerns Regarding AI (N = 34) Risk Domain Mean (SD) Bias & Fairness 3.80 (0.72) Privacy & Data Security 3.63 (0.78) Academic Integrity 3.85 (0.72) Accountability 3.69 (0.75) Faculty Readiness for AI Integration Readiness scores indicated increasing levels of preparedness. Awareness and knowledge received high ratings (M = 4.01, SD = 0.66). Preparedness and Skills were rated at a moderate level, but Institutional Support scores indicated robust institutional support for AI integration (M = 3.82). Group Comparisons Group comparisons exhibited notable differences among faculty subgroups. Faculty members who had completed AI training indicated higher levels of PU and Readiness than those who had not participated in training ( t = 3.01, p = 0.005; t = 2.82, p = 0.008). Lecturers demonstrated greater concerns regarding academic integrity in comparison to assistant or associate professors ( F = 4.12, p = 0.014), while faculty with over 15 years of teaching experience reported a lower perception of ease of use ( t = –2.07, p = 0.045). These findings are illustrated in Table 4. Table 4. Group Comparisons of Faculty Perceptions and Readiness by Demographic Characteristics Outcome Comparison Group sizes Test ( df ) Value Effect size p (Holm) Perceived Usefulness Trained vs Not Trained 11 vs 23 t (32) 3.01 d = 1.10 0.005 Readiness Trained vs Not Trained 11 vs 23 t (32) 2.82 d = 1.03 0.008 Academic Integrity Concerns Lecturer vs Assistant vs Associate 9 / 21 / 4 F (2, 31) 4.12 ηp² = 0.21 0.014 Perceived Ease of Use ≤15 yrs vs >15 yrs Not reported t (32) –2.07 r = 0.34 0.045 Correlation Analysis The analysis of Pearson correlation indicated a significant positive relationship between Opportunities and Readiness ( r = 0.64, p < 0.001), while Risks showed a negative correlation with Readiness ( r = –0.28, p = 0.046). Social Influence and Institutional Support exhibited the highest correlations with Readiness (both r = 0.69, p < 0.001). The complete set of correlation coefficients is presented in Table 5. Table 5. Pearson Correlation Matrix of Key Variables Variable Opportunities Risks Readiness Social Influence Institutional Support 1. Opportunities 1.00 –0.22 0.64‡ 0.58‡ 0.61‡ 2. Risks –0.22 1.00 –0.28† –0.19 –0.16 3. Readiness 0.64‡ –0.28† 1.00 0.69‡ 0.69‡ 4. Social Influence 0.58‡ –0.19 0.69‡ 1.00 0.72‡ 5. Institutional Support 0.61‡ –0.16 0.69‡ 0.72‡ 1.00 † p < 0.05, ‡ p < 0.001 Factor Analysis Exploratory factor analysis revealed a two-factor model comprising Opportunities and Risks, characterised by factor loadings exceeding 0.60 and strong internal consistency (α = 0.83 for Opportunities and α = 0.81 for Risks). Table 6 contains comprehensive information on item loadings and the variance explained. Table 6. Exploratory Factor Analysis of Faculty Perceptions of AI Opportunities and Risks Item Factor 1: Opportunities Factor 2: Risks h² Perceived Usefulness 0.72–0.85 < 0.30 (suppressed) 0.52–0.73 Perceived Ease of Use 0.70–0.81 < 0.30 (suppressed) 0.49–0.66 Performance Expectancy 0.78–0.84 < 0.30 (suppressed) 0.56–0.71 Effort Expectancy 0.69–0.75 < 0.30 (suppressed) 0.46–0.60 Social Influence 0.73–0.80 < 0.30 (suppressed) 0.53–0.64 Facilitating Conditions 0.71–0.76 < 0.30 (suppressed) 0.50–0.58 Bias & Fairness < 0.30 (suppressed) 0.68–0.79 0.47–0.62 Privacy & Data Security < 0.30 (suppressed) 0.70–0.82 0.49–0.67 Academic Integrity < 0.30 (suppressed) 0.72–0.85 0.52–0.72 Accountability < 0.30 (suppressed) 0.74–0.81 0.51–0.65 Eigenvalues: Opportunities = 5.62; Risks = 4.87 Variance explained: 62.3% Reliability: Opportunities α = 0.83; Risks α = 0.81 Note. Non-primary factor loadings less than 0.30 are marked as “< 0.30 (suppressed)” by convention; no cross-loadings of 0.30 or higher were detected. Discussion Principal findings In this cross-sectional analysis involving nursing faculty, participants indicated a strong perception of opportunities for AI in both assessment and teaching, particularly in terms of performance expectancy and social influence. They expressed moderate concerns regarding risks, with the highest level of apprehension related to academic integrity. Readiness among faculty was primarily linked to perceived opportunities and the overall institutional environment. Those who had undergone AI training reported greater PU and readiness, while more seasoned faculty indicated lower ease of use; additionally, lecturers highlighted more integrity concerns compared to their senior counterparts. Collectively, these tendencies imply that enhancing capabilities and providing institutional support are crucial factors for the responsible implementation of AI in nursing assessment. Our findings align with recent studies in nursing education, which generally reflect affirmative—yet cautious—faculty perceptions regarding the educational benefits of AI, coupled with concerns related to ethics and integrity. Nursing educators frequently acknowledge the advantages of adaptive learning, simulations, and analytics while also noting concerns surrounding fairness and privacy [14]. Research based on the TAM indicates that nursing students perceive a strong sense of usefulness and ease of use, supporting the advantageous aspect we have also identified [15]. The TAM/UTAUT framework applied in our research aligns well with existing findings in both medical and higher education contexts: performance expectancy, effort expectancy, social influence, and facilitating conditions serve as strong predictors of adoption and readiness [8]. The significant connection we noted between social influence and institutional support with readiness resonates with UTAUT findings, where organisational encouragement and resources enhance both behavioural intention and actual usage [16]. This is consistent with sector-wide analyses (e.g., BEME Guide No.84; 2024), which indicate that contextual facilitators (such as policies, tools, training, and governance) frequently influence whether successful pilot programmes are adopted into standard practice [3]. On the risk side, the integrity concerns among faculty reflect various systematic reviews indicating that generative AI makes authorship, originality, and verification more complex, leading to demands for reassessing assessments and establishing transparent academic integrity policies, rather than relying solely on detection [17]. Recent reviews show that work produced by AI can bypass traditional grading methods, highlighting the necessity for more genuine assessments, transparency, and the inclusion of oral/practical elements instead of solely depending on detection [17]. Ethical evaluations in education also highlight concerns related to privacy, surveillance, and biases in algorithms—issues that are significant in our area of risk, necessitating the establishment of policy frameworks and risk assessments prior to implementation in contexts where high-stakes evaluations occur [18]. Research across various sectors in medicine also underscores that trust depends on transparency, explainability, accountability, and governance—elements that probably influence faculty's readiness [2]. The results of this research indicate that training correlates with an increased sense of usefulness, while readiness aligns with nursing education data that connects faculty development to enhanced confidence and intentions to adopt new practices [19]. It is also consistent with acceptance frameworks where skills and facilitating conditions lower effort costs and perceived risks, thus altering the cost-benefit analysis in favour of adoption [16]. While prior AI training was associated with higher perceived usefulness and readiness, more experienced faculty reported lower ease of use, suggesting the need for tailored training (such as role-specific, embedded in workflows, and practical) may be required to prevent widening disparities among faculty members. Ultimately, the two-factor framework (opportunities versus risks) aligns with findings from scoping reviews: the body of evidence on AI in education is bimodal, highlighting both pedagogical advantages and efficiency improvements while also raising unresolved issues concerning fairness, privacy, and integrity—particularly in assessment situations where the stakes are significant and incentives for misuse are considerable [20]. Implications for nursing assessment and faculty development Redesigning assessments to match their intended purpose. When learning objectives permit, emphasise authentic, iterative, and dialogic assessments (such as oral examinations, OSCE-style stations for theoretical application, supervised laboratory tasks, reflective materials with evidence of the process, and code walkthroughs) to minimise the potential for simple replacement by generative tools and to clearly instruct ethical AI usage. Clear governance. Creating transparent programme-specific policies that outline acceptable AI usage, disclosure requirements, privacy protections, model and feature evaluations, and accountability structures—ensuring alignment with institutional integrity standards and local laws. Focused skill development. Providing a range of training options (from basic to advanced), workshops centred on assessments, and peer demonstrations to enhance perceptions of AI's ease of use and value—particularly for senior faculty or those who are less confident with technology. Infrastructure and assistance. Allocating resources to establish conducive conditions (such as secure, approved technologies; support for instructional design; example rubrics; and sandbox environments) that facilitate compliant and effective AI integration into teaching. Continuous evaluation. Setting up programme-level dashboards to monitor the adoption of AI, faculty and student sentiments, integrity issues, and equity effects; combine quantitative measures with qualitative insights to progressively improve practices. Strengths and limitations This research provides evidence specific to nursing education, uses validated acceptance frameworks (TAM/UTAUT), and reports construct validity alongside reliability measures and factor structure. However, the cross-sectional design was conducted at a single site, and the limited sample size restricts generalizability and causal conclusions; consequently, the findings should be viewed as correlational rather than causal. Self-reported assessments may introduce potential common-method bias and effects related to social desirability. While exploratory factor analysis reveals the internal structure, replication using larger, multi-site samples (and confirmatory models) is necessary. Future research objectives should focus on multi-institutional and longitudinal studies that connect faculty readiness with objective adoption and evaluation outcomes; experimental or quasi-experimental assessments of training programmes; and mixed-methods research to explore how discipline-specific assessment tasks (such as paediatrics, community health, and simulation) influence both opportunities and risks. Given ongoing concerns regarding integrity and fairness, studies should examine the effectiveness, equity among students, and scalability of design patterns (such as staged submissions, oral defences, and AI-use disclosure). Conclusion The study demonstrates that nursing faculty recognise significant opportunities for incorporating AI tools into their teaching, particularly in assessment activities, while also maintaining valid concerns related to academic integrity, privacy, bias, and accountability. The readiness to adopt AI was most closely linked to perceived opportunities and influenced by the institutional environment (including social influence and institutional support). Faculty development played a crucial role: those with prior AI training perceived the material as more useful and more ready to apply it. On the other hand, more experienced faculty noted a decreased ease of use, indicating the necessity for differentiated, workflow-integrated training. Evidence of construct validity indicated a two-factor structure (opportunities and risks) with strong internal consistency. Since readiness is influenced by perceived advantages and the institutional environment, initiatives should combine distinct governance (acceptable use, privacy protections, sanctioned tools) with targeted, workflow-integrated faculty training—especially for veteran staff members. Overall, these results suggest that faculty readiness for AI in assessment can be enhanced by synchronising faculty development with strong governance and designing assessments that maximise AI’s benefits while reducing its risks. Declarations Acknowledgement Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R876), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Funding This work is supported by Princess Nourah bint Abdulrahman University (PNU) for project number (PNURSP2025R876). Competing interests The author declares no competing interests. Data availability The data that support the findings of this study are available from the corresponding author, [Z.A.A.], upon reasonable request. References Rani S, Kumari A, Ekka SC, Chakraborty R, Ekka S. Perception of Medical Students and Faculty Regarding the Use of Artificial Intelligence (AI) in Medical Education: A Cross-Sectional Study. Cureus. 2025;17(1). 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Abusabeib","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYHACNhCRwMDMw/gAIpBAvBZmAxK1MPCwSRClRb798LPHPH/s8vjZeY9VF+44zMDPnmPA+KMGtxbGnjRzY9625GLJZr602zPPHGaQ7HljwMxzDLcWZoYEM2neBubEDYd5zG7zth1mMLiRY8AMcS0Oj/A//ybN86c+cT9QSzFIi/0NkMP+4dbCI5FjJs3DdjhxAzOPGTPYFokcAwbeNtxaJCTelBvObTteLHGYL1l6Zls6j8SZZwWHeftwa5HvT9/24M2f6jz+/rMHPxe2WcvxtydvfPjjG24tKIAZ5FIQ4wCRGiBaRsEoGAWjYBRgAABA20mjsZL8aAAAAABJRU5ErkJggg==","orcid":"","institution":"Princess Nourah bint Abdulrahman University","correspondingAuthor":true,"prefix":"","firstName":"Zeinab","middleName":"A.","lastName":"Abusabeib","suffix":""}],"badges":[],"createdAt":"2025-09-27 14:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7729091/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7729091/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94380399,"identity":"1badc638-f1a4-4a91-8de7-96b4497779d6","added_by":"auto","created_at":"2025-10-27 13:41:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54291,"visible":true,"origin":"","legend":"","description":"","filename":"AssessingtheIntegrationofArtificialIntelligenceToolsinNursingEducationCopy.docx","url":"https://assets-eu.researchsquare.com/files/rs-7729091/v1/5cb5532d815387a081e16b2c.docx"},{"id":94380874,"identity":"1795524e-a991-410c-8777-274dff17c237","added_by":"auto","created_at":"2025-10-27 13:42:03","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5047,"visible":true,"origin":"","legend":"","description":"","filename":"f740d5e0b66e40138cf97e9823311023.json","url":"https://assets-eu.researchsquare.com/files/rs-7729091/v1/d1951c4e3b841cf29622e4a3.json"},{"id":94379747,"identity":"a7f3a4ae-a6c0-46fc-a439-311eb7d617f8","added_by":"auto","created_at":"2025-10-27 13:40:17","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78009,"visible":true,"origin":"","legend":"","description":"","filename":"f740d5e0b66e40138cf97e98233110231enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7729091/v1/2e85ec4479149898c735c6cb.xml"},{"id":94380642,"identity":"728f68bd-0f03-4559-b151-bb576d6e0eb8","added_by":"auto","created_at":"2025-10-27 13:41:40","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76048,"visible":true,"origin":"","legend":"","description":"","filename":"f740d5e0b66e40138cf97e98233110231structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7729091/v1/419e0954de81ec5c4ac73787.xml"},{"id":94380583,"identity":"3e1949f7-9f57-47a9-9611-ae3ed14b83a7","added_by":"auto","created_at":"2025-10-27 13:41:33","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83438,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7729091/v1/d939364601712dc759e25c3a.html"},{"id":94456686,"identity":"c2d26b3a-20b8-42af-bafd-5c22a140054a","added_by":"auto","created_at":"2025-10-27 14:44:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":672086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7729091/v1/17b83422-5588-4257-abd5-afa81d9bae5d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Faculty Perceptions of Artificial Intelligence Tools in Nursing Education in Saudi Arabia: Opportunities, Risks, and Readiness for Assessment Integration","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial Intelligence (AI) is revolutionising healthcare education by providing innovative resources that can enhance the processes of teaching, learning, and assessment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the realm of nursing education, applications of AI include virtual simulations, intelligent tutoring systems, as well as automated grading and feedback systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. AI-driven tools have demonstrated potential in enhancing student engagement and academic success, providing personalised feedback, facilitating self-directed learning, and supporting educators in creating interactive and adaptive learning environments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the incorporation of AI in educational contexts presents certain challenges. Faculty members are crucial to the adoption and effective use of AI tools; however, their perceptions, readiness, and concerns remain underexplored, particularly within the context of nursing education in Saudi Arabia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Grasping these elements is vital for the effective integration of AI technologies into nursing curricula [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent research has indicated the possible advantages of AI in medical education, which include tailored learning experiences, enhanced accuracy in assessments, and improved efficiency in administrative duties [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For example, Rani et al. (2025) investigated the views of medical students and faculty regarding AI in medical education, highlighting its significance for enhancing curricula [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Likewise, Lee (2025) employed the Technology Acceptance Model (TAM) to analyse faculty acceptance of AI tools in medical education [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite such progress, apprehensions about the ethical repercussions of AI\u0026mdash;such as bias, privacy concerns, and threats to academic integrity\u0026mdash;continue to exist [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Salih et al. (2024) discovered that while faculty recognised the advantageous effects of AI on medical education, they also voiced concerns about its ethical dimensions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These issues are particularly relevant in nursing education, where the humane aspects of care and ethical decision-making are fundamental themes in the curriculum [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Saudi Arabia, the use of AI in nursing education is still in its nascent stages. Alshanberi et al. (2024) evaluated the knowledge and attitudes toward AI among faculty and students at a medical college in Saudi Arabia, underscoring the necessity for more research [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Comprehending faculty perceptions and readiness is critical for creating targeted interventions that address their concerns and encourage the successful integration of AI tools in nursing education [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis research focuses on filling the evidence gap regarding the perceptions of nursing faculty on AI for assessment in Saudi Arabia by (i) assessing perceived opportunities and risks, (ii) analysing readiness for AI implementation, (iii) evaluating differences among subgroups based on training, rank, and experience, and (iv) investigating construct validity (through exploratory factor analysis and reliability).\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cem\u003eDesign\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study employs a cross-sectional survey design and utilises Google Forms to collect data from faculty members at a nursing college in Saudi Arabia. The survey instrument is adapted from well-established models, including the TAM [12] and the UTAUT [13], to evaluate faculty perceptions regarding the use of AI tools in education. Additionally, Likert-scale items are implemented to measure perceived risks associated with AI, such as bias, privacy issues, and threats to academic integrity [2, 11].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eArea and Setting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe research was conducted at a nursing college in Saudi Arabia, providing context-specific insights into faculty perceptions of AI integration in nursing education. The environment comprises a diverse group of faculty members with varying levels of experience and familiarity with AI technologies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe key variables include perceived opportunities (assessed through TAM and UTAUT constructs like Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and performance expectancy), perceived risks (evaluated through Likert-scale items concerning bias, privacy, and academic integrity), and faculty readiness (measured by self-reported willingness to adopt AI tools in assessment practices).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe focus population consists of faculty members at the nursing college who are involved in teaching and assessment. These faculty members directly engage with assessment practices and are likely to possess informed views on the integration of AI. During the 2025–2026 academic year, all faculty members working at the nursing college who were engaged in teaching and assessment and willing to participate were included in the study. Faculty on extended leave, those on sabbatical, those not participating in assessment activities, or those unwilling to be involved, were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Collection Techniques\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData collection was performed via an online survey using Google Forms. The questionnaire comprised four sections: (1) Demographics; (2) Opportunities and acceptance of AI, assessed through validated items from the TAM [12] and the UTAUT [13], (3) Risks and concerns, measured using items adapted from the Trust in AI in Healthcare Applications Scale [2] and the AI Ethical Concerns in Education Questionnaire \u0026nbsp;[11]; and (4) Readiness, evaluated using the validated AI Readiness in Higher Education Scale [6].\u003c/p\u003e\n\u003cp id=\"_Toc207303590\"\u003e\u003cem\u003eData processing and analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA pilot test was conducted with 10 faculty members from a different health college within the same institution to assess the clarity, readability, and cultural appropriateness of the items. Normality (Shapiro–Wilk) and homogeneity of variances (Levene’s) were examined. Internal consistency was evaluated using Cronbach’s alpha. Construct validity was examined through an exploratory factor analysis prior to the main analysis.\u003c/p\u003e\n\u003cp\u003eData were analysed using SPSS version 26. Descriptives (mean, standard deviation, frequency) summarised sample characteristics and study variables. Differences between groups were examined through independent-samples t-tests or one-way ANOVA, with Holm-adjusted post-hoc analyses conducted when necessary; effect sizes are reported as Cohen’s d or partial η² along with 95% confidence intervals. The relationships among Opportunities, Risks, Readiness, Social Influence, and Institutional Support were explored using Pearson correlations. All negatively phrased (reversed) items were reverse-coded before analysis to guarantee consistency in their interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was obtained from the Princess Nourah bint Abdulrahman University Institutional Review Board, Riyadh, Saudi Arabia (PNURSP2025R876).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eElectronic informed consent was obtained prior to participation. Participants have the right to withdraw at any time without facing any consequences. All data were securely stored and used exclusively for research purposes.\u003c/p\u003e\n\u003cp\u003eThe study adhered to the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript contains no individual person’s data (images, videos, or potentially identifying clinical details).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePilot Testing and Data Validation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA pilot test was carried out involving 10 faculty members from a different health college within the same institution. Feedback was used to improve the wording while preserving the core concepts. The adequacy of the sample was favourable (KMO = 0.82), and the results of Bartlett’s test of sphericity were significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Both parallel analysis and the scree plot indicated retention of two factors—Opportunities and Risks, which together accounted for 62.3% of the total variance. Each of the targeted items demonstrated loading of at least 0.60 on its respective factor, with no cross-loadings exceeding 0.30. The internal consistency of the subscales varied, with α values ranging from 0.78 to 0.87.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDemographic Characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 34 faculty members participated in the survey, resulting in a response rate of 85%. The average age was 41.9 years. The academic ranks included Lecturers (n = 9, 26.5%), Assistant Professors (n = 21, 61.8%), and Associate Professors (n = 4, 11.7%). Teaching experience varied from 3 to 25 years, with an average of 11.7 years. Approximately 75% of respondents reported prior use of AI tools in teaching or assessment, while 32.4% indicated that they had received formal training related to AI (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Faculty Demographics (N = 34)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003en (%) / Mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.9 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eAcademic Rank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e– Lecturer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e– Assistant Professor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (61.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e– Associate Professor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTeaching Experience (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.7 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrevious AI use in teaching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25 (73.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-related training received\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eFaculty Perceptions of AI Opportunities\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFaculty perceptions demonstrated a strong acknowledgement of AI’s potential. The scores for PU and PEOU were 3.93 (SD = 0.70) and 3.74 (SD = 0.69), respectively. The construct with the highest rating was Performance Expectancy (M = 4.04, SD = 0.67). Notably, Social Influence also received a high rating (M = 3.91, SD = 0.72), indicating substantial institutional backing for the integration of AI in education (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2. Faculty Perceptions of AI Opportunities (N = 34)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Usefulness (PU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.93 (0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Ease of Use (PEOU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.74 (0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerformance Expectancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.04 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffort Expectancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.84 (0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Influence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.91 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFacilitating Conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.62 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ePerceived Risks of AI in Assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFaculty members showed moderate levels of concern regarding potential risks. As shown in Table 3, the most significant concern pertained to Academic Integrity (M = 3.85, SD = 0.72), followed by concerns about Bias \u0026amp; Fairness (M = 3.80, SD = 0.72). The apprehensions regarding Privacy and Accountability were somewhat lower.\u003c/p\u003e\n\u003cp\u003eTable 3. Faculty Concerns Regarding AI (N = 34)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBias \u0026amp; Fairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.80 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivacy \u0026amp; Data Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.63 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Integrity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.85 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccountability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.69 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eFaculty Readiness for AI Integration\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eReadiness scores indicated increasing levels of preparedness. Awareness and knowledge received high ratings (M = 4.01, SD = 0.66). Preparedness and Skills were rated at a moderate level, but Institutional Support scores indicated robust institutional support for AI integration (M = 3.82).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGroup Comparisons\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGroup comparisons exhibited notable differences among faculty subgroups. Faculty members who had completed AI training indicated higher levels of PU and Readiness than those who had not participated in training (\u003cem\u003et\u003c/em\u003e = 3.01, \u003cem\u003ep\u003c/em\u003e = 0.005; \u003cem\u003et\u003c/em\u003e = 2.82, \u003cem\u003ep\u003c/em\u003e = 0.008). Lecturers demonstrated greater concerns regarding academic integrity in comparison to assistant or associate professors (\u003cem\u003eF\u003c/em\u003e = 4.12, \u003cem\u003ep\u003c/em\u003e = 0.014), while faculty with over 15 years of teaching experience reported a lower perception of ease of use (\u003cem\u003et\u003c/em\u003e = –2.07, \u003cem\u003ep\u003c/em\u003e = 0.045). These findings are illustrated in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4. Group Comparisons of Faculty Perceptions and Readiness by Demographic Characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGroup sizes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTest (\u003cem\u003edf\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEffect size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(Holm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Usefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrained vs Not Trained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 vs 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003et\u0026nbsp;\u003c/em\u003e(32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ed\u003c/em\u003e = 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eReadiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrained vs Not Trained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 vs 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ed\u0026nbsp;\u003c/em\u003e= 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Integrity Concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLecturer vs Assistant vs Associate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 / 21 / 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e (2, 31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eηp² = 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Ease of Use\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e≤15 yrs vs \u0026gt;15 yrs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e = 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCorrelation Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of Pearson correlation indicated a significant positive relationship between Opportunities and Readiness (\u003cem\u003er\u003c/em\u003e = 0.64, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while Risks showed a negative correlation with Readiness (\u003cem\u003er\u003c/em\u003e = –0.28, \u003cem\u003ep\u003c/em\u003e = 0.046). Social Influence and Institutional Support exhibited the highest correlations with Readiness (both \u003cem\u003er\u003c/em\u003e = 0.69, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The complete set of correlation coefficients is presented in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Pearson Correlation Matrix of Key Variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003col\u003e\n \u003cli\u003eOpportunities\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eRisks\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003eReadiness\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003eSocial Influence\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003eInstitutional Support\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e1. Opportunities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.64‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.58‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.61‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2. Risks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–0.28†\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3. Readiness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.64‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–0.28†\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.69‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.69‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e4. Social Influence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.58‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.69‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.72‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e5. Institutional Support\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.61‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e–0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.69‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.72‡\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e† p \u0026lt; 0.05, ‡ p \u0026lt; 0.001\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFactor Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eExploratory factor analysis revealed a two-factor model comprising Opportunities and Risks, characterised by factor loadings exceeding 0.60 and strong internal consistency (α = 0.83 for Opportunities and α = 0.81 for Risks). Table 6 contains comprehensive information on item loadings and the variance explained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Exploratory Factor Analysis of Faculty Perceptions of AI Opportunities and Risks\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFactor 1: Opportunities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFactor 2: Risks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eh²\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Usefulness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72–0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.52–0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerceived Ease of Use\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.70–0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49–0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerformance Expectancy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78–0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.56–0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffort Expectancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.69–0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46–0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSocial Influence\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.73–0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.53–0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFacilitating Conditions\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71–0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.50–0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBias \u0026amp; Fairness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68–0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.47–0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivacy \u0026amp; Data Security\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.70–0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49–0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcademic Integrity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72–0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.52–0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAccountability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.30 (suppressed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74–0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51–0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eEigenvalues: Opportunities = 5.62; Risks = 4.87\u003cbr\u003e\u0026nbsp;Variance explained: 62.3%\u003cbr\u003e\u0026nbsp;Reliability: Opportunities α = 0.83; Risks α = 0.81\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. Non-primary factor loadings less than 0.30 are marked as “\u0026lt; 0.30 (suppressed)” by convention; no cross-loadings of 0.30 or higher were detected.\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003ePrincipal findings\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this cross-sectional analysis involving nursing faculty, participants indicated a strong perception of opportunities for AI in both assessment and teaching, particularly in terms of performance expectancy and social influence. They expressed moderate concerns regarding risks, with the highest level of apprehension related to academic integrity. Readiness among faculty was primarily linked to perceived opportunities and the overall institutional environment. Those who had undergone AI training reported greater PU and readiness, while more seasoned faculty indicated lower ease of use; additionally, lecturers highlighted more integrity concerns compared to their senior counterparts. Collectively, these tendencies imply that enhancing capabilities and providing institutional support are crucial factors for the responsible implementation of AI in nursing assessment. Our findings align with recent studies in nursing education, which generally reflect affirmative—yet cautious—faculty perceptions regarding the educational benefits of AI, coupled with concerns related to ethics and integrity. Nursing educators frequently acknowledge the advantages of adaptive learning, simulations, and analytics while also noting concerns surrounding fairness and privacy [14]. Research based on the TAM indicates that nursing students perceive a strong sense of usefulness and ease of use, supporting the advantageous aspect we have also identified [15].\u003c/p\u003e\n\u003cp\u003eThe TAM/UTAUT framework applied in our research aligns well with existing findings in both medical and higher education contexts: performance expectancy, effort expectancy, social influence, and facilitating conditions serve as strong predictors of adoption and readiness [8]. The significant connection we noted between social influence and institutional support with readiness resonates with UTAUT findings, where organisational encouragement and resources enhance both behavioural intention and actual usage [16]. This is consistent with sector-wide analyses (e.g., BEME Guide No.84; 2024), which indicate that contextual facilitators (such as policies, tools, training, and governance) frequently influence whether successful pilot programmes are adopted into standard practice [3].\u003c/p\u003e\n\u003cp\u003eOn the risk side, the integrity concerns among faculty reflect various systematic reviews indicating that generative AI makes authorship, originality, and verification more complex, leading to demands for reassessing assessments and establishing transparent academic integrity policies, rather than relying solely on detection [17]. Recent reviews show that work produced by AI can bypass traditional grading methods, highlighting the necessity for more genuine assessments, transparency, and the inclusion of oral/practical elements instead of solely depending on detection [17]. Ethical evaluations in education also highlight concerns related to privacy, surveillance, and biases in algorithms—issues that are significant in our area of risk, necessitating the establishment of policy frameworks and risk assessments prior to implementation in contexts where high-stakes evaluations occur [18]. Research across various sectors in medicine also underscores that trust depends on transparency, explainability, accountability, and governance—elements that probably influence faculty's readiness [2].\u003c/p\u003e\n\u003cp\u003eThe results of this research indicate that training correlates with an increased sense of usefulness, while readiness aligns with nursing education data that connects faculty development to enhanced confidence and intentions to adopt new practices [19]. It is also consistent with acceptance frameworks where skills and facilitating conditions lower effort costs and perceived risks, thus altering the cost-benefit analysis in favour of adoption [16]. While prior AI training was associated with higher perceived usefulness and readiness, more experienced faculty reported lower ease of use, suggesting the need for tailored training (such as role-specific, embedded in workflows, and practical) may be required to prevent widening disparities among faculty members.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUltimately, the two-factor framework (opportunities versus risks) aligns with findings from scoping reviews: the body of evidence on AI in education is bimodal, highlighting both pedagogical advantages and efficiency improvements while also raising unresolved issues concerning fairness, privacy, and integrity—particularly in assessment situations where the stakes are significant and incentives for misuse are considerable [20].\u003c/p\u003e\n\u003cp\u003eImplications for nursing assessment and faculty development\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eRedesigning assessments to match their intended purpose. When learning objectives permit, emphasise authentic, iterative, and dialogic assessments (such as oral examinations, OSCE-style stations for theoretical application, supervised laboratory tasks, reflective materials with evidence of the process, and code walkthroughs) to minimise the potential for simple replacement by generative tools and to clearly instruct ethical AI usage.\u003c/li\u003e\n \u003cli\u003eClear governance. Creating transparent programme-specific policies that outline acceptable AI usage, disclosure requirements, privacy protections, model and feature evaluations, and accountability structures—ensuring alignment with institutional integrity standards and local laws.\u003c/li\u003e\n \u003cli\u003eFocused skill development. Providing a range of training options (from basic to advanced), workshops centred on assessments, and peer demonstrations to enhance perceptions of AI's ease of use and value—particularly for senior faculty or those who are less confident with technology.\u003c/li\u003e\n \u003cli\u003eInfrastructure and assistance. Allocating resources to establish conducive conditions (such as secure, approved technologies; support for instructional design; example rubrics; and sandbox environments) that facilitate compliant and effective AI integration into teaching.\u003c/li\u003e\n \u003cli\u003eContinuous evaluation. Setting up programme-level dashboards to monitor the adoption of AI, faculty and student sentiments, integrity issues, and equity effects; combine quantitative measures with qualitative insights to progressively improve practices.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eStrengths and limitations\u003c/p\u003e\n\u003cp\u003eThis research provides evidence specific to nursing education, uses validated acceptance frameworks (TAM/UTAUT), and reports construct validity alongside reliability measures and factor structure. However, the cross-sectional design was conducted at a single site, and the limited sample size restricts generalizability and causal conclusions; consequently, the findings should be viewed as correlational rather than causal. Self-reported assessments may introduce potential common-method bias and effects related to social desirability. While exploratory factor analysis reveals the internal structure, replication using larger, multi-site samples (and confirmatory models) is necessary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture research objectives should focus on multi-institutional and longitudinal studies that connect faculty readiness with objective adoption and evaluation outcomes; experimental or quasi-experimental assessments of training programmes; and mixed-methods research to explore how discipline-specific assessment tasks (such as paediatrics, community health, and simulation) influence both opportunities and risks. Given ongoing concerns regarding integrity and fairness, studies should examine the effectiveness, equity among students, and scalability of design patterns (such as staged submissions, oral defences, and AI-use disclosure).\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study demonstrates that nursing faculty recognise significant opportunities for incorporating AI tools into their teaching, particularly in assessment activities, while also maintaining valid concerns related to academic integrity, privacy, bias, and accountability. The readiness to adopt AI was most closely linked to perceived opportunities and influenced by the institutional environment (including social influence and institutional support). Faculty development played a crucial role: those with prior AI training perceived the material as more useful and more ready to apply it. On the other hand, more experienced faculty noted a decreased ease of use, indicating the necessity for differentiated, workflow-integrated training. Evidence of construct validity indicated a two-factor structure (opportunities and risks) with strong internal consistency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince readiness is influenced by perceived advantages and the institutional environment, initiatives should combine distinct governance (acceptable use, privacy protections, sanctioned tools) with targeted, workflow-integrated faculty training—especially for veteran staff members. Overall, these results suggest that faculty readiness for AI in assessment can be enhanced by synchronising faculty development with strong governance and designing assessments that maximise AI’s benefits while reducing its risks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R876), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by Princess Nourah bint Abdulrahman University (PNU) for project number (PNURSP2025R876).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, [Z.A.A.], upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRani S, Kumari A, Ekka SC, Chakraborty R, Ekka S. 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AI in higher education: Faculty perspective towards artificial intelligence through UTAUT approach. Ho Chi Minh City Open Univ J Science-Social Sci. 2024;14(4):32\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBittle K, El-Gayar O. Generative AI and academic integrity in higher education: A systematic review and research agenda. Information. 2025;16(4):296.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMishara P. The ethical implications of AI in education: privacy, bias, and accountability. J Inf Educ Res. 2024;4:3550.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEhmke SD, Bridges J, Patel SE. Self-perceived knowledge, skills, and attitude of nursing faculty on generative artificial intelligence in nursing education: A descriptive, cross-sectional study. Teaching and Learning in Nursing; 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShaw K, Henning MA, Webster CS. Artificial intelligence in medical education: a scoping review of the evidence for efficacy and future directions. Med Sci Educ. 2025:1\u0026ndash;14.\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":false,"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":"artificial intelligence, nursing education, assessment, faculty perceptions, readiness, Saudi Arabia","lastPublishedDoi":"10.21203/rs.3.rs-7729091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7729091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eArtificial intelligence (AI) is rapidly entering higher education; however, research regarding nursing faculty perceptions\u0026mdash;especially concerning assessment\u0026mdash;remains scarce in Saudi Arabia. Grasping the perceived opportunities, risks, and level of preparedness is crucial for the responsible implementation of these technologies.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eTo explore nursing faculty members\u0026rsquo; perceptions of AI opportunities and risks, as well as their readiness for integrating AI into assessments; to identify variations among groups based on training, rank, and experience; and to evaluate the construct validity of the measurement model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional online survey was conducted among all faculty at a Saudi nursing college (N\u0026thinsp;=\u0026thinsp;34; 85% response) during the academic year 2025\u0026ndash;2026. Measures were adapted from the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), as well as published instruments on AI risks and readiness. Analyses included descriptive statistics, independent \u003cem\u003et\u003c/em\u003e-tests/ANOVA, Pearson correlations, and exploratory factor analysis, including an assessment of internal consistency.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFaculty indicated a strong perception of opportunities (e.g., Performance Expectancy, M\u0026thinsp;=\u0026thinsp;4.04, SD\u0026thinsp;=\u0026thinsp;0.67) but expressed moderate concerns regarding risks, with the highest score being for Academic Integrity. Readiness showed a positive correlation with opportunities (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and with social influence/institutional support (both \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while it exhibited a negative correlation with risks (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). Faculty who received training perceived greater usefulness (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and higher readiness (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008); lecturers revealed greater integrity concerns than senior faculty members; faculty with over 15 years of experience reported a lower level of ease of use. The factor analysis confirmed a two-factor arrangement (opportunities, risks) with a high level of internal consistency.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eNursing faculty exhibit a positive attitude toward the use of AI in assessments but stress the importance of safeguards to ensure integrity and privacy. Readiness is influenced by the perceived advantages and the institutional environment, indicating the need for governance, approved tools, and focused professional development\u0026mdash;particularly for faculty members with greater experience. Programmes should integrate redesigning assessments with clear policies and building capacity to promote responsible and equitable AI adoption. Multi-site, longitudinal studies assessing training and policy initiatives are recommended.\u003c/p\u003e","manuscriptTitle":"Faculty Perceptions of Artificial Intelligence Tools in Nursing Education in Saudi Arabia: Opportunities, Risks, and Readiness for Assessment Integration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-25 07:42:02","doi":"10.21203/rs.3.rs-7729091/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-31T05:59:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218091680530555593077563194827024111170","date":"2025-10-26T10:22:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-15T14:38:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T15:58:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24378169231175265058092229385514803872","date":"2025-10-12T04:22:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212703354390033583436936070672518843951","date":"2025-10-11T23:38:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-11T20:24:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-10T04:22:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T09:29:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-04T17:55:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-10-04T15:42:06+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":"733e449e-2eba-4174-8a63-9c1b071ce019","owner":[],"postedDate":"October 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-25T07:42:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-25 07:42:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7729091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7729091","identity":"rs-7729091","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}