Effects of generative artificial intelligence (GenAI) patient simulation on clinical competency among global nursing undergraduates: A cross-over randomised controlled trial

Effects of generative artificial intelligence (GenAI) patient simulation on clinical competency among global nursing undergraduates: A cross-over randomised controlled trial | 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 Effects of generative artificial intelligence (GenAI) patient simulation on clinical competency among global nursing undergraduates: A cross-over randomised controlled trial Tai Chun John Fung, Siu Ling Chan, Choi Fung Lam, Chung Yan Lam, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6250414/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jul, 2025 Read the published version in BMC Nursing → Version 1 posted 14 You are reading this latest preprint version Abstract Background and aims Clinical competency is paramount for nurses to ensure that patients receive safe, high-quality care. Generative artificial intelligence (GenAI) in nursing education is gaining attention, and evidence shows its suitability for real-life situations. GenAI may be an effective solution for enhancing nurses’ clinical competency. This study compared the impact of scenario-based GenAI patient simulation versus immersive 360° virtual reality (VR) simulation on educational outcomes, namely clinical competence, cultural awareness, AI readiness, and simulation effectiveness. Methods This cross-over randomised controlled study design was conducted from June 2024 to August 2024. Forty-four undergraduate nursing students in years 1, 2, and 3 were selected to participate. Subgroups were formed, each comprising three undergraduate nursing students from different years. They were randomised to receive either a GenAI patient simulation (intervention, Group B) or 360° VR simulation (control, Group A) for three separate days and with a washout period. Four self-reported questionnaires were used to measure clinical competency: the Clinical Competence Questionnaire (CCQ), Cultural Awareness Scale (CAS), Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), and Simulation Effectiveness Tool – Modified Questionnaire (SET-M). Results The study revealed notable improvements in clinical competence and confidence among the participants. Group A demonstrated significant enhancements in the CCQ at both time points, and Group B also showed meaningful progress. Both groups experienced changes in the CAS-Total scores, although these changes were not statistically significant. In terms of the MAIRS-MS total score, Group A had a significant increase at time 1 (T1), and Group B showed an improvement from baseline to time 2 (cross-over session, T2). Regarding SET-M results, most participants (75%) felt that debriefing contributed to their learning, and 77.3% reported increased confidence in their nursing assessment skills. Conclusions The findings offer compelling evidence of its effectiveness in enhancing clinical outcomes, as assessed by the CCQ, CAS, and MAIRS-MS. Importantly, our results reveal statistically significant improvements in these measures, particularly within Group B. Both 360° VR simulation and GenAI patient simulation with real-time feedback and GenAI debriefing can serve as powerful teaching tools for improving nursing students’ clinical outcomes; however, GenAI exhibits a notably greater effect. Clinical trial registration/number Not applicable Generative artificial intelligence Patient simulation Clinical reasoning 360-degree virtual reality Clinical competence Medical language Nursing education Randomised controlled trial Introduction The integration of artificial intelligence (AI) into healthcare has prompted a growing emphasis in the literature on the necessity of AI readiness among nursing students and professionals. AI readiness encompasses the knowledge, skills, and attitudes required for the effective application of AI in healthcare, including preventive measures, diagnostics, treatment, and comprehensive care delivery [1]. However, the potential for AI to perpetuate biases and adversely affect patient care has been noted, emphasizing the need for rigorous training and oversight [2]. To mitigate these risks, nursing curricula should proactively foster AI readiness through targeted education on AI fundamentals, its strengths and limitations, and ethical considerations. The literature supports curricular innovations like dedicated coursework and simulation-based learning with GenAI to build foundational AI readiness and reduce biases in healthcare [3, 4]. Further research is required to systematically assess various pedagogical approaches using AI and their long-term effects. Background The increasing integration of artificial intelligence (AI) into healthcare has underscored the necessity for nursing students and professionals to develop AI readiness [ 5 ]. This readiness is crucial as AI technologies, including virtual reality (VR) and conversational agents, are becoming integral to nursing education, enhancing learning experiences by providing immersive and interactive environments [ 6 ]. Notably, 360° VR videos simulate health and social care scenarios that are typically inaccessible in traditional educational settings, thereby significantly improving communication skills and self-efficacy in AI-enabled simulations. However, there are concerns regarding AI's perceived human-like qualities, which could impact the authenticity of these simulations [ 7 , 8 ]. Building on this foundation, pilot studies have demonstrated that integrating 360° VR with structured debriefing through learning management systems can enhance students' clinical competence and decision-making confidence [ 9 , 10 ]. This approach not only fosters a deeper understanding of clinical scenarios but also prepares students for the complexities of AI-driven healthcare environments. The emergence of Generative AI (GenAI) patient simulation represents a novel trend in nursing education, necessitating targeted education on AI fundamentals, its strengths, limitations, and ethical considerations [ 11 ]. The literature further emphasizes the importance of stakeholder engagement and a comprehensive understanding of AI's clinical role [ 12 ]. This includes the integration of coursework that addresses AI biases, ensuring that future nurses are equipped to navigate the ethical and practical challenges posed by AI technologies [ 13 ]. While text-to-text GenAI tools like ChatGPT offer educational benefits, caution is advised due to potential risks such as AI hallucination, misinformation, and cultural sensitivity issues [ 14 ]. These findings advocate for urgent curricular reform to prepare nurses for AI practice, highlighting the need for a balanced approach that leverages AI's potential while mitigating its risks [ 15 , 16 ]. Simulation-based GenAI learning has been shown to enhance AI readiness and reinforce core nursing concepts [ 17 ]. Students exposed to GenAI-generated cases exhibit improved clinical reasoning compared to those trained through traditional methods [ 18 ]. This suggests that GenAI can play a pivotal role in preparing nursing students for real-world clinical challenges. However, further research is imperative to optimize the integration of GenAI into nursing education while ensuring that training remains bias-free. In summary, the adoption of AI in nursing education is accelerating, necessitating improved competency among students and faculty. Current literature supports curricular innovations such as dedicated coursework and GenAI simulations to build AI readiness and mitigate biases. Future research should systematically evaluate various AI pedagogical approaches and their long-term impacts, ensuring that nursing education evolves in tandem with technological advancements in healthcare. Methods Study Design This was a cross-over randomised controlled study with two intervention arms: GenAI and 360° VR simulation. The participants were allocated equally to the study arms. Study Participants Forty-four undergraduate nursing students from Hong Kong, Taiwan, Thailand, Spain, South Korea, and Australia in years 1, 2, and 3 of their studies participated. Subgroups were created, each consisting of three undergraduate nursing students from various academic years. A total of 22 participants were randomly assigned to Group A (who received the 360° VR simulation first and GenAI second), and the other 22 participants were assigned to Group B (who received GenAI first and the 360° VR simulation second). The allocation process was randomised with a 1:1 allocation ratio. This sequence was generated by an independent researcher to ensure unbiased assignment and was concealed from both groups of participants and the researchers involved in the study. Moreover, all of the participants provided written informed consent before the study, and the voluntary nature of participation and the confidentiality of the data were strongly emphasised throughout the process. This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (IRB number: UW 24–396). Data Collection The participants participated in the simulation program over the course of three distinct days, followed by a washout period. Five questionnaire tools were used to collect data: the Clinical Competence Questionnaire (CCQ), the Cultural Awareness Scale (CAS), the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), the Simulation Effectiveness Tool – Modified (SET-M), and a demographic questionnaire. The questionnaires were administered to the participants at three time points – pre-intervention (baseline [T0]), time 1 (T1), and time 2 (cross-over session) (T2). CCQ The 47-item CCQ, originally developed by Liou, was utilized in this study to assess participants’ self-perceived clinical competency at both T0 and T1 [ 19 ]. The questionnaire consists of four competency components, namely nursing professional behaviours (NP; 16 items, score range: 16–90), general performance (GP; 13 items, score range: 13–65), core nursing skills (CNS; 12 items, score range: 12–60), and advanced nursing skills (ANS; 6 items, score range: 6–30). The items are scored using a 5-point Likert scale (1 = ‘Do not have a clue’; 2 = ‘Know in theory, but not confident at all in practice’; 3 = ‘Know in theory, can perform some parts in practice independently, and need supervision to be readily available’; 4 = ‘Know in theory, competent in practice, need contactable sources of supervision’; 5 = ‘Know in theory, competent in practice without supervision’). The total score ranges from 47 to 235 [ 19 ]. A higher score indicates higher perceived competence. Cronbach’s alpha for the CCQ was 0.98, with the reliability of each component ranging from 0.87 to 0.95 [ 20 ]. CAS The CAS was designed by Rew to measure the cultural awareness of nursing students [ 21 ]. The CAS is based on the pathway model and comprises 36 items divided into five subscales: general education experience, cognitive awareness of attitudes, classroom and clinical instruction, research issues, and clinical practice. The reliability was .91 for students and .82 for faculty. The subscales’ reliability values ranged between .66 and .99 for students and between .56 and .87 for faculty [ 21 ]. MAIRS-MS The MAIRS-MS instrument, initially created by Karaca, requires participants to rate their self-assessment on a 5-point Likert scale (ranging from 1 = ‘strongly disagree’ to 5 = ‘strongly agree’) on 22 statements regarding their AI readiness [ 22 ]. The MAIRS-MS is subdivided into four factors: cognition (8 items), ability (8 items), vision (3 items), and ethics (3 items). Two exemplary items are ‘I can explain how AI systems are trained’ (cognition) and ‘I can explain the limitations of AI technology’ (vision) [ 22 ]. The internal consistency of the overall scale to be acceptable (Cronbach’s alpha = 0.88), and the Cronbach’s alphas for the individual factors were 0.83 (cognition), 0.77 (ability), 0.72 (vision), and 0.63 (ethics) [ 23 ]. SET-M The participants’ confidence was measured using the SET-M at T1 only. The modified version of this instrument was published in 2015, with a total of 19 items and a 3-point response scale [ 24 ]. The items are divided into four domains: pre-briefing, learning, confidence, and debriefing. Cronbach’s alpha for the overall SET-M was .94, with the reliability of each domain ranging from .83 to .91 [ 24 ]. Demographic questionnaire A questionnaire was used to collect the participants’ demographic data, including gender, year of study, and previous clinical experience. Intervention Details The participants engaged in two distinct clinical scenarios: a pneumonia case at T1 and an appendicitis case at T2. For both interventions, the same clinical cases were utilised; however, they were approached differently to ensure a fair comparison of the educational outcomes. This design allowed for an evaluation of the effectiveness of each method in enhancing the nursing students’ ability to prioritise nursing diagnoses, a critical skill in delivering high-quality patient care. Components of GenAI Patient Simulation The proposed GenAI Patient Simulation system for nursing education integrates several key components to enhance the learning experience. Firstly, it categorizes nursing diagnoses into distinct domains such as physiological, psychosocial, and cultural, further classifying them into actual, potential, and wellness diagnoses to provide a structured approach to patient care assessment. Secondly, the system incorporates Maslow’s hierarchy of needs, aligning nursing diagnoses with the five levels of human needs to prioritize care, thereby enriching students' theoretical understanding and guiding practical decision-making. Thirdly, it employs a standardized scale to assess the severity and complexity of disease conditions, considering factors like symptom acuity, potential for rapid deterioration, and intervention urgency, which aids students in identifying critical nursing diagnoses. Finally, the system establishes care priorities through a scoring mechanism that evaluates each diagnosis based on its impact on patient safety, potential complications, and overall outcomes, ensuring informed clinical decision-making. Implementation of Constructive Feedback through GenAI debriefing The implementation of constructive feedback through GenAI debriefing in the proposed system is designed to facilitate asynchronous learning by providing students with personalized, real-time feedback post-simulation. This process involves reflective practice where students analyze their responses, actions, and the outcomes of their clinical decisions. GenAI evaluates the rationale behind each nursing diagnosis, assessing the alignment with established grading frameworks to highlight areas of strength and improvement, thereby deepening students' understanding of prioritization. Additionally, it scrutinizes the actions taken during simulations, offering feedback on the appropriateness of interventions to enhance clinical judgment and critical thinking. Finally, GenAI assesses the results of these actions, providing insights into the effectiveness of interventions based on chosen diagnoses, reinforcing evidence-based practice. This debriefing not only offers expert feedback on the correct prioritization of nursing diagnoses but also generates a score based on the accuracy of assessments and choices, ensuring a comprehensive learning experience. Implementation of 360° VR The immersive 360° VR experience facilitated decision-making and interaction within the video by capturing the students’ timestamps on a learning management system. The The 360° VR videos content was parallel to the GenAI patient simulation. Students participated in a debriefing session led by trained faculty members. This debriefing process enabled vicarious peer learning in which the students reviewed each other’s timestamps and comments. Importantly, the platform’s structured debriefing feature provides guidance for educators, allowing them to conduct a systematic debriefing process. These functionalities ensure that both educators and students receive timely and relevant feedback in a structured manner. The realism of the scenarios presented in the VR videos ensures that the educational content remains aligned with the objectives of the intervention, thereby fostering a deeper understanding and retention of knowledge among students. Data Analysis Descriptive statistics, including means and standard deviations, were used to summarise the participants’ demographics and scores on the CCQ, CAS, MAIRS-MS, and SET-M. The baseline characteristics of the intervention and control groups were compared using chi-square tests or Fisher’s exact tests for categorical data and independent t-tests for continuous data. A linear mixed-model analysis was conducted to analyse the CCQ, CAS, and MAIRS-MS scores. This model treated the participants as a random effect and included time, group, randomisation sequence order, and the time–group interaction as fixed effects [ 25 ]. Linear contrasts were used to assess both between-group differences and within-group changes over time. All of the statistical analyses were performed using IBM SPSS version 29.0 (IBM Corp., Armonk, NY, USA). Two-sided tests were used throughout, with a significance level of p < 0.05. Results Descriptive results (Table 1 ) Table 1 Baseline Characteristic of the Participants (n = 44) Participants, No (%) Characteristic All (N = 44) A group (N = 22) B group (N = 22) P value Age, mean (SD) 21.0 (1.24) 21.1 (1.27) 20.9 (1.23) 0.632 Sex 0.216 Male 7 (38.3) 5 (22.7) 2 (9.1) Female 37 (61.7) 17 (77.3) 20 (90.9) Year of study 0.627 Year 1 2 (4.5) 1 (4.5) 1 (4.5) Year 2 15 (34.1) 6 (27.3) 9 (40.9) Year 3 27 (61.4) 15 (68.2) 12 (54.5) Do you have any TUNS experience? 0.262 Yes 9 (20.5) 3 (13.6) 6 (27.3) No 35 (79.5) 19 (86.4) 16 (72.7) Have you attended any clinical practicum? 1.000 Yes 28 (73.6) 14 (73.6) 14 (73.6) No 8 (36.4) 8 (36.4) 8 (36.4) Do you have any GenAI training before? 0.240 Yes 8 (18.2) 6 (27.3) 2 (9.1) No 36 (81.8) 16 (72.7) 20 (90.9) CCQ Total Score, mean (SD) 154.16 (32.82) 157.23 (31.38) 151.09 (34.66) 0.542 Nursing Professional Behaviors, mean (SD) 52.25 (13.99) 52.82 (13.97) 51.68 (14.33) 0.791 General Performance, mean (SD) 46.50 (6.40) 46.68 (5.97) 46.32 (6.94) 0.853 Core Nursing Skills, mean (SD) 37.91 (11.20) 39.18 (11.23) 36.64 (11.28) 0.457 Advanced Nursing Skills, mean (SD) 17.50 (5.50) 18.55 (4.90) 16.45 (5.97) 0.211 CAS Total Score, mean (SD) 189.43 (40.15) 190.91 (42.38) 187.95 (38.74) 0.810 General Educational Experience 74.75 (16.69) 74.00 (17.46) 75.50 (16.27) 0.770 Cognitive Awareness 38.86 (8.42) 38.05 (8.38) 39.68 (8.58) 0.526 Research Issue 20.77 (5.17) 20.41 (5.32) 21.14 (5.10) 0.646 Behaviors or Comfort with Interactions 25.73 (8.71) 27.36 (9.08) 24.09 (8.19) 0.216 Patient Care or Clinical Issues 29.32 (7.69) 31.09 (7.76) 27.55 (7.38) 0.128 MAISMS Total Score, mean (SD) 60.27 (14.45) 61.00 (17.41) 59.55 (11.10) 0.743 Cognition 22.30 (6.11) 22.77 (7.08) 21.82 (5.09) 0.610 Ability 21.77 (5.85) 21.86 (6.65) 21.68 (5.07) 0.919 Vision 8.05 (2.40) 8.27 (2.68) 7.82 (2.13) 0.536 Ethics 8.16 (2.78) 8.09 (2.78) 8.23 (2.84) 0.873 In total, 44 participants were included in the study, divided equally into two groups (A group: n = 22; B group: n = 22). The mean age of participants was 21.0 ± 1.24 years, with no significant difference between groups (P = 0.632). Most participants were female (84.1%), and the distribution of sex did not differ significantly between groups (P = 0.216). Regarding the year of study, most participants were in Year 2 (34.1%), and there was no significant difference between groups (P = 0.627). Participants with prior TUNS experience comprised 20.5%, while those without accounted for 79.5%, with no significant difference between groups (P = 0.262). Similarly, the majority (73.6%) had attended clinical practicum, and this characteristic was evenly distributed across groups (P = 1.000). Only a minority (18.2%) had GenAI training experience, with no significant difference between groups (P = 0.240). Baseline scores for CCQ Total Score, Nursing Professional Behaviors, General Performance, Core Nursing Skills, and Advanced Nursing Skills were comparable between groups, with P values ranging from 0.211 to 0.853, indicating no significant differences. The CAS Total Score also showed no significant difference between groups (P = 0.810). These findings suggest that demographic and baseline clinical characteristics were well-balanced across the two groups (P > 0.05) (Table 1 ). Effects of the Interventions on CCQ (Table 2 ) Table 2 Mixed effects analysis for the interventional effects (n = 44) Measure Group A(n = 22) Within-Group Change (95% CI) P Value Group B(n = 22) Within-Group Change (95% CI) P Value Between-Group Difference Mean (95% CI) P Value CCQ-Total T1 24.95 (13.96, 35.95) < 0.001* 47.68 (36.68, 58.68) < 0.001* 16.59 (2.77, 30.41) 0.020* T2 31.09 (20.09, 42.09) < 0.001* 39.64 (28.64, 50.64) < 0.001* 2.41 (-11.41, 16.23) 0.727 CCQ-NP T1 8.82 (3.70, 13.94) < 0.001* 11.86 (6.74, 16.98) < 0.001* 1.91 (-5.64, 9.45) 0.612 T2 12.50 (7.38, 17.62) < 0.001* 13.59 (8.47, 18.71) < 0.001* -0.05 (-7.59, 7.50) 0.990 CCQ-GP T1 3.36 (0.45, 6.28) 0.024* 9.41 (6.49, 12.33) < 0.001* 5.68 (2.72, 8.64) < 0.001* T2 3.73 (0.81, 6.65) 0.013* 6.77 (3.85, 9.69) < 0.001* 2.68 (-0.28, 5.64) 0.075 CCQ-CNS T1 9.23 (5.06, 13.40) < 0.001* 18.14 (13.96, 22.31) < 0.001* 6.36 (2.08, 10.65) 0.005* T2 9.36 (5.19, 13.54) < 0.001* 13.82 (9.65, 17.99) < 0.001* 1.91 (-2.38, 6.20) 0.374 CCQ-ANS T1 3.55 (1.31, 5.78) 0.002* 8.27 (6.04, 10.51) < 0.001* 2.64 (-0.39, 5.66) 0.086 T2 5.50 (3.27, 7.73) < 0.001* 5.45 (3.22, 7.69) < 0.001* -2.14 (-5.16, 0.89) 0.162 CAS-Total T1 18.27 (0.22, 36.32) 0.047* 27.59 (9.54, 45.64) 0.003* 6.36 (-15.08, 27.80) 0.552 T2 15.32 (-2.73, 33.37) 0.095 20.91 (2.86, 38.96) 0.024* 2.64 (-18.80, 24.08) 0.805 CAS-GEE T1 3.95 (-3.92, 11.83) 0.321 5.41 (-2.46, 13.28) 0.175 2.95 (-6.70, 12.61) 0.540 T2 3.32 (-4.55, 11.19) 0.404 2.50 (-5.37, 10.37) 0.529 0.68 (-8.97, 10.34) 0.887 CAS-CA T1 4.77 (1.02, 8.53) 0.013* 6.09 (2.33, 9.85) 0.002* 2.95 (-1.01, 6.92) 0.140 T2 4.23 (0.47, 7.98) 0.028* 6.05 (2.29, 9.80) 0.002* 3.45 (-0.51, 7.42) 0.086 CAS-RI T1 2.68 (-0.08, 5.44) 0.057 2.05 (-0.72, 4.81) 0.145 0.09 (-3.17, 3.36) 0.955 T2 0.64 (-2.13, 3.40) 0.648 1.50 (-1.26, 4.26) 0.283 1.59 (-1.67, 4.86) 0.331 CAS-BOCWI T1 2.86 (-1.16, 6.89) 0.161 5.59 (1.57, 9.62) 0.007* -0.55 (-5.43, 4.34) 0.823 T2 2.68 (-1.34, 6.71) 0.189 4.00 (-0.03, 8.03) 0.051 -1.95 (-6.84, 2.93) 0.424 CAS-PCOCI T1 4.00 (0.55, 7.45) 0.023* 8.45 (5.01, 11.90) < 0.001* 0.91 (-3.00, 4.82) 0.641 T2 4.45 (1.01, 7.90) 0.012* 6.86 (3.42, 10.31) < 0.001* -1.14 (-5.04, 2.77) 0.560 MAISMS-Total T1 16.64 (9.80, 23.47) < 0.001* 30.18 (23.35, 37.01) < 0.001* 12.09 (4.43, 19.75) 0.003* MAISMS-Cognition T1 4.50 (1.46, 7.54) 0.005* 11.59 (8.55, 14.63) < 0.001* 6.14 (2.89, 9.38) < 0.001* MAISMS-Ability T1 6.00 (2.25, 9.75) 0.002* 10.36 (6.62, 14.11) < 0.001* 4.18 (0.13, 8.23) 0.043* MAISMS-Vision T1 3.23 (1.79, 4.66) < 0.001* 4.55 (3.11, 5.98) < 0.001* 0.86 (-0.60, 2.33) 0.241 MAISMS-Ethics T1 2.91 (1.63, 4.19) < 0.001* 3.68 (2.40, 4.96) < 0.001* 0.91 (-0.53, 2.35) 0.209 Note : ● CCQ : Clinical Competence Questionnaire ● NP : Nursing professional behaviors ● GP : General Performance ● CNS : Core Nursing Skills ● ANS : Advanced Nursing Skills ● CAS : Cultural Awareness Scale ● GEE : General Educational Experience ● CA : Cognitive Awareness ● RI : Research Issues ● BOCWI : Behavior or Comfort with Interactions ● PCOCI : Patient Care or Clinical Issues *p < 0.05 indicates statistical significance. Table 2 shows the results of mixed-effect tests of the interventions’ effects on clinical competence. The results indicate significant improvements across various measures for both groups. For the CCQ-Total, both Group A and B demonstrated notable within-group improvements at both T1 (Group A: 24.95, 95% CI [13.96, 35.95], p < 0.001; Group B: 47.68, 95% CI [36.68, 58.68], p < 0.001) and T2 (Group A: 31.09, 95% CI [20.09, 42.09], p < 0.001; Group B: 39.64, 95% CI [28.64, 50.64], p < 0.001). The between-group difference was statistically significant at T1 (16.59, 95% CI [2.77, 30.41], p = 0.020). In the NP component, both groups showed significant improvements at both time points (Group A at T1: 8.82, 95% CI [3.70, 13.94], p < 0.001; Group A at T2: 12.50, 95% CI [7.38, 17.62], p < 0.001; Group B at T1: 11.86, 95% CI [6.74, 16.98], p < 0.001; Group B at T2: 13.59, 95% CI [8.47, 18.71], p < 0.001). For the GP component, both Groups A and B showed significant improvements at T1 (Group A: 3.36, 95% CI [0.45, 6.28], p = 0.024; Group B: 9.41, 95% CI [6.49, 12.33], p < 0.001) and T2 (Group A: 3.73, 95% CI [0.81, 6.65], p = 0.013; Group B: 6.77, 95% CI [3.85, 9.69], p < 0.001). In the CNS component, both Groups A and B showed significant improvements at T1 (Group A: 9.23, 95% CI [5.06, 13.40], p < 0.001; Group B: 18.14, 95% CI [13.96, 22.31], p < 0.001) and T2 (Group A: 9.36, 95% CI [5.19, 13.54], p < 0.001; Group B: 13.82, 95% CI [9.65, 17.99], p < 0.001). Regarding the ANS component, both groups exhibited significant improvements at both time points (Group A at T1: 3.55, 95% CI [1.31, 5.78], p = 0.002; Group A at T2: 8.27, 95% CI [6.04, 10.51], p < 0.001; Group B at T1: 5.50, 95% CI [3.27, 7.73], p < 0.001; Group B at T2: 5.45, 95% CI [3.22, 7.69], p < 0.001). Notably, the between-group differences were significant at T1 for the GP component (5.68, 95% CI [2.72, 8.64], p < 0.001) and CNS component (6.36, 95% CI [2.08, 10.65], p = 0.005), with Group B demonstrating substantially higher scores than Group A in these domains (Table 2 ). Effects of the Interventions on CAS (Table 2 ) Table 2 shows the results of mixed-effect tests of the interventions’ effects on cultural awareness. In terms of the total score, Group A improved significantly at T1 (18.27, 95% CI [0.22, 36.32], p = 0.047) and marginally significantly at T2 (15.32, 95% CI [-2.73, 33.37], p = 0.095), and Group B improved significantly both at T1 (27.59, 95% CI [9.54, 45.64], p = 0.003) and T2 (20.91, 95% CI [2.86, 38.96], p = 0.024). Both groups demonstrated significant improvements in the Cognitive Awareness measure at both T1 and T2 (Group A at T1: 4.77, 95% CI [1.02, 8.53], p = 0.013; Group A at T2: 4.23, 95% CI [0.47, 7.98], p = 0.028; Group B at T1: 6.09, 95% CI [2.33, 9.85], p = 0.002; Group B at T2: 6.05, 95% CI [2.29, 9.80], p = 0.002). For the Patient Care or Clinical Issues measure, both Group A and Group B showed significant improvements at T1 (Group A: 4.00, 95% CI [0.55, 7.45], p = 0.023; Group B: 8.45, 95% CI [5.01, 11.90], p < 0.001) and T2 (Group A: 4.45, 95% CI [1.01, 7.90], p = 0.012; Group B: 6.86, 95% CI [3.42, 10.31], p < 0.001) (Table 2 ). No significant within-group changes or between-group differences were observed for the General Educational Experience, Research Issue and Behavior, or Comfort with Interactions measures. Effects of the Interventions on MAIRS-MS (Table 2 ) Table 2 shows the results of mixed-effect tests of the interventions’ effects on the MAIRS-MS. In the MAIRS-MS Total Score, Group A exhibited significant changes from T0 to T1 with an increase of 16.64 (95% CI [9.80, 23.47], p < 0.001), while Group B showed a significant change from T0 to T1 with an increase of 30.18 (95% CI [23.35, 37.01], p < 0.001). A significant between-group difference was noted (12.09, 95% CI [4.43, 19.75], p = 0.003) (Table 2 ). All of the other subscales, namely Cognition, Ability, Vision, and Ethics, demonstrated significant increases, as shown in Table 2 . Significant between-group differences were observed in Cognition and Ability. Overall, both groups demonstrated significant improvements. Effect of the Interventions on SET-M (Table 3 ) Table 3 Simulation Effectiveness Tool-Modified (SET‐M) responses. Simulation Effectiveness Tool-Modified (SET‐M) responses All respondents (N = 44) Survey questions: Strongly Agree Somewhat Agree Do Not Agree Prebriefing subscale Prebriefing increased my confidence. 52.3% (23) 47.7% (21) 0% (0) Prebriefing was beneficial to my learning. 65.9% (29) 34.1% (15) 0% (0) Learning subscale I am better prepared to respond to changes in my patient’s condition. 70.5% (31) 29.5% (13) 0% (0) I developed a better understanding of the pathophysiology. 68.2% (30) 31.8% (14) 0% (0) I am more confident of my nursing assessment skills. 68.2% (30) 31.8% (14) 0% (0) I felt empowered to make clinical decisions. 77.3% (34) 22.7% (10) 0% (0) I developed a better understanding of medications. 65.9% (29) 31.8% (14) 2.3% (1) I had the opportunity to practice my clinical decision making skills. 70.5% (31) 29.5% (13) 0% (0) Confidence subscale I am more confident in my ability to prioritize care and interventions 68.2% (30) 31.8% (14) 0% (0) I am more confident in communicating with my patient. 72.7% (32) 27.3% (12) 0% (0) I am more confident in my ability to teach patients about their illness and interventions. 70.5% (31) 29.5% (13) 0% (0) I am more confident in my ability to report information to health care team. 68.2% (30) 31.8% (14) 0% (0) I am more confident in providing interventions that foster patient safety. 75.0% (33) 25.0% (11) 0% (0) I am more confident in using evidence-based practice to provide nursing care. 72.7% (32) 27.3% (12) 0% (0) Debriefing subscale Debriefing contributed to my learning. 75.0% (33) 25.0% (11) 0% (0) Debriefing allowed me to verbalize my feelings before focusing on the scenario. 68.2% (30) 31.8% (14) 0% (0) Debriefing was valuable in helping me improve my clinical judgment. 68.2% (30) 31.8% (14) 0% (0) Debriefing provided opportunities to self-reflect on my performance during simulation. 68.2% (30) 31.8% (14) 0% (0) Debriefing was a constructive evaluation of the simulation. 75.0% (33) 25.0% (11) 0% (0) Table 3 shows the responses of the participants to the SET-M. Most of the participants (75%, n = 33) strongly agreed that debriefing benefited their learning. Regarding learning confidence, over three quarters of the participants (77.3%, n = 43) strongly agreed that their nursing assessment skills had improved. Additionally, most of the participants (75.0%, n = 33) strongly agreed that they were confident in performing handovers to the healthcare team and using evidence-based practice to provide nursing care (Table 3 ). Discussion This study explored the impact of two innovative interventions – GenAI and 360° VR simulation – on clinical competence, cultural awareness, and AI readiness among nursing students in two distinct groups. Group A received 360° VR Simulation first, followed by GenAI, while Group B experienced these interventions in the reverse order. Both groups demonstrated significant improvements in clinical competence, with Group B initially showing advantages in general performance and core nursing skills. Regarding cultural awareness, Group B made earlier gains in educational experience and cognitive awareness, although Group A eventually matched these improvements. Notably, both groups exhibited substantial progress in AI readiness across all subscales. Additionally, a majority of students reported positive experiences with debriefing, contributing to increased confidence in their nursing skills. Both 360° VR simulation and GenAI patient simulation are therefore effective pedagogical strategies for enhancing the clinical outcomes of nursing students. However, GenAI evidently demonstrates a more pronounced impact, as its initial implementation tends to engage students more effectively in the learning process, leading to improved educational results. Specifically, when GenAI patient simulation is provided first, it yields greater outcomes; subsequently utilising 360° VR can enhance these results. Clinical Competence The results of the study indicate that the intervention enhanced clinical competence in both groups, with Group B demonstrating a more pronounced initial improvement at T1, particularly in the General Practice (GP) and Clinical Nursing Skills (CNS) components of the Clinical Competence Questionnaire (CCQ). However, by T2, Group A had begun to catch up, suggesting that while their progress was initially slower, they eventually reached comparable levels of competence. This implies that the intervention had a more gradual impact on some participants but was ultimately effective for both groups over time. The customisation enabled by GenAI interventions, which catered to individual learners' needs, preferences, and learning styles, likely facilitated targeted feedback, adaptive challenges, and content alignment with specific clinical roles and responsibilities. By providing tailored learning experiences, GenAI effectively supported the development of essential clinical competencies in both general practice and specialised nursing fields [ 26 ]. Moreover, GenAI interventions specifically targeted cognitive skills such as problem-solving, critical thinking, decision-making, and clinical reasoning, which are crucial for effective practice in healthcare settings [ 27 ]. Engaging participants in higher-order thinking tasks and complex scenarios relevant to their practice areas potentially enhanced the development of these cognitive skills more effectively than traditional interventions [ 28 ]. A notable feature of GenAI is its ability to deliver real-time feedback, debriefing, performance monitoring, and adaptive learning pathways tailored to learners' responses and progress. This immediate feedback loop allows participants to identify areas for improvement, adjust their strategies, and monitor their growth over time, fostering an environment conducive to continuous learning and skill refinement. The personalised feedback and adaptive nature of GenAI likely contributed to the improved performance outcomes observed in the GP and CNS assessments of the CCQ. By leveraging advanced AI technologies, including deep learning and natural language processing, GenAI creates dynamic, responsive, and intelligent learning environments that optimise educational experiences, customise content delivery, and support individualised learning pathways focused on the specific competencies required for diverse nursing specialties [ 29 ]. Cultural Awareness The interventions aimed at enhancing cultural awareness, as measured by the Cultural Awareness Scale (CAS), demonstrated significant improvements in both groups, particularly in the dimensions of Cognitive Awareness and Patient Care or Clinical Issues. Group B exhibited more substantial enhancements at both time points compared to Group A, indicating a potentially more effective intervention for this group. Nonetheless, both groups experienced significant gains in several core areas of cultural competence. The notable advancements in Cognitive Awareness and Patient Care or Clinical Issues underscore the interventions' efficacy in improving participants' understanding of cultural issues and their practical application in clinical settings. This is particularly crucial in healthcare, where cultural competence is increasingly recognized as vital for delivering high-quality, patient-centered care [ 30 ]. The balanced representation of students from various countries in both groups facilitated an increase in cultural awareness through GenAI patient simulations. These simulations were designed to interact according to distinct cultural beliefs and express cultural requirements, allowing students to engage in and gain insights from culturally nuanced exchanges, thereby enhancing their comprehension of cultural diversity within healthcare environments. The improvements in these areas suggest that the intervention successfully addressed key components of cultural competence, potentially leading to better patient outcomes and more culturally sensitive care practices. However, the absence of significant changes in the areas of Behavior or Comfort with Interactions and Research Issues across both groups highlights a critical gap in the interventions. These components of cultural competence, which involve interpersonal dynamics and behavior in diverse settings, may necessitate more teaching sessions, such as role-playing, simulations, or real-world applications, to cultivate practical skills in navigating cultural interactions. This suggests that while cognitive and contextual aspects of cultural awareness can be effectively enhanced through the existing interventions, relational and behavioral dimensions might require more intensive or prolonged engagement to produce measurable improvements [ 31 ]. The findings indicate that interventions focusing on cultural awareness can significantly enhance healthcare professionals' cognitive understanding and clinical application of cultural competence. Such improvements are essential for fostering culturally sensitive healthcare environments, particularly in increasingly diverse patient populations. However, to achieve broader and more comprehensive cultural competence, future training programs should incorporate more specific elements related to research issues and comfort with intercultural interactions, areas that were not significantly impacted in this study. AI Readiness The findings indicate that the intervention was effective in enhancing AI readiness for both groups, with Group B demonstrating a higher score than Group A. The significant improvements observed across all subscales of the MAIRS-MS suggest that the interventions were highly effective in increasing healthcare professionals' readiness to engage with and utilize AI in their practice. The notable between-group differences across all subscales imply that while both interventions were effective, Group B may have benefited from additional resources or a more conducive learning environment, resulting in greater gains. These results emphasize the importance of tailored interventions that can address varying levels of baseline readiness among healthcare professionals. The results indicate that both interventions successfully addressed key aspects of AI readiness, including cognitive understanding of AI concepts, the ability to interact with AI systems, vision for AI integration in healthcare, and ethical considerations surrounding AI use. The substantial increases in scores, particularly in the Cognition and Ability subscales, highlight that the participants gained theoretical knowledge about AI and practical skills in working with AI technologies. This comprehensive improvement across all dimensions of AI readiness is crucial for the successful implementation of AI in healthcare settings [ 32 , 33 ]. Perceived Simulation Effectiveness (SET-M) The analysis of the SET-M responses indicates that a majority of the participants perceived debriefing as highly beneficial to their learning, with 75% expressing strong agreement. Furthermore, a large majority of the participants reported increased confidence in their nursing assessment skills (77.3%) and handover performance (75.0%). These findings underscore the importance of perceived competence and confidence in clinical training, suggesting that structured peer interactions may enhance learning experiences and foster a supportive educational environment. Future research should investigate the long-term effects of these interventions on clinical competence through various methodologies. Longitudinal studies could provide insights into the sustainability of improvements over time, and comparative effectiveness research might help determine the relative impact of GenAI versus traditional educational methods. Qualitative inquiries into student experiences and perceptions could enrich our understanding of the mechanisms underlying the observed enhancements. Additionally, exploring a broader range of outcome measures, including patient care outcomes and the transferability of skills to real-world clinical settings, could inform future research efforts aimed at refining nursing education and practice. By addressing these areas, we can better understand how innovative educational interventions can shape the future of nursing training and ultimately improve patient care. Conclusion The results provide robust evidence for the effectiveness of this cutting-edge approach in improving perceived clinical competence, cultural awareness, and AI readiness, with statistically significant improvements observed, particularly within Group B. Both 360° VR simulation and GenAI patient simulation were demonstrated as effective pedagogical strategies for enhancing the clinical outcomes of nursing students. However, GenAI exhibited a more significant impact, as its initial implementation engaged students more effectively in the learning process, thereby facilitating enhanced educational outcomes. Specifically, when GenAI patient simulation was introduced first, it produced superior results; the subsequent use of 360° VR amplified these benefits. Both groups exhibited marked improvements across all assessed domains, underscoring the intervention’s efficacy in clinical training. The favourable feedback regarding the SET-M further underscores that the participating students not only recognised the benefits of these GenAI simulations but also valued the function of GenAI debriefing, which enriched their educational experiences and facilitated their professional development. The integration of GenAI into patient simulations facilitates dynamic and adaptive learning environments, enabling learners to engage with realistic scenarios that reflect the complexities of actual clinical encounters. Through real-time feedback and GenAI debriefing, this technology improves diagnostic accuracy and supports critical skills such as decision-making and cultural competence, which are essential elements in today’s diverse healthcare landscape. To fully capitalise on these promising results, future research should concentrate on identifying the specific components of our GenAI intervention that drive these positive outcomes. By doing so, we can enhance educational strategies in clinical training, ensuring that healthcare professionals are not only technically proficient but also culturally aware and prepared for the challenges of contemporary medical practice. This exploration will be crucial in advancing healthcare education through innovative simulation methodologies. Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. Ethical approval for this study was obtained from the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (IRB number: UW 24-396). Participants were ensured of data confidentiality and voluntariness of participation in and withdrawal from the study and personal written informed consent was obtained from each of them. Consent for publication Not applicable Availability of data and materials The datasets used and analyzed during the present study are available from the corresponding author on reasonable request. Competing interests The authors declare no conflict of interest. Funding The study was funded by the HKU Teaching Development Grant (Grant no.: 966). The funders did not have a role in study design, data collection, analysis, reporting or the decision to submit for publication. Authors' contributions John Fung Tai Chun: Conceptualization, Methodology, Supervision, Writing- original draft and review & editing. Siu Ling Chan: Conceptualization, Methodology, Writing- review & editing. Choi Fung Lam: Methodology, Project administration, Writing- review & editing. Chung Yan Lam: Methodology, Project administration, Writing- review & editing. Christopher Chi Wai Cheng: Methodology, Formal analysis, Writing- review & editing. Man Hin Lai: Methodology, Formal analysis, Project administration, Data Curation, Visualization, Writing- review & editing. Cheuk Chun Joseph Ho: Project administration, Writing- review & editing. Au Siu Lun: Project administration, Writing- review & editing. Mak Lok Yi: Project administration, Writing- review & editing. Sophie Hu: Project administration, Writing- review & editing. Supapak Phetrasuwan: Project administration, Writing- review & editing. Jumpee Granger: Project administration, Writing- review & editing. Jung Min Yoon: Project administration, Writing- review & editing. Gulzar Mailk: Project administration, Writing- review & editing. Clara Cabrera Moreno: Project administration, Writing- review & editing. Patrick Kwok Man Hei: Project administration, Writing- review & editing. Chia-Chin Lin (corresponding author): Project administration, Writing- review & editing. Acknowledgements We are grateful for the nursing students participating in the study. Author’s information Authors and Affiliations Tai Chun John Fung a* , Siu Ling Chan b , Choi Fung Lam c , Chung Yan Lam d , Christopher Chi Wai Cheng e , Man Hin Lai f , Chun Joseph Ho Cheuk g , Siu Lun Au h , Lok Yi Mak i , Sophie Hu j , Supapak Phetrasuwan k , Jumpee Granger l , Jung Min Yoon m , Gulzar Mailk n , Clara Cabrera Moreno o , Man Hei Patrick Kwok p , Chia-Chin Lin (corresponding author) q* a, b, c, d, f, g, h, p, q University of Hong Kong, Hong Kong e The Chinese University of Hong Kong, Hong Kong i Hong Kong Baptist University, China j National Yang Ming Chiao Tung University, Taiwan k Mahidol University, Thailand l Ramathibodi School of Nursing, Thailand m EWHA University, Korea, Democratic People's Republic of n La Trobe University, Australia o University of Navarra, Spain * Corresponding author Corresponding authors: Fung Tai Chun John & Chia-Chin Lin Corresponding authors’ email address: [email protected] & [email protected] References Laupichler, M. 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Navarra","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"Cabrera","lastName":"Moreno","suffix":""},{"id":450805440,"identity":"84f8ccce-5dd8-4d4f-b503-3c0e6017f540","order_by":15,"name":"Man Hei Patrick Kwok","email":"","orcid":"","institution":"University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"Hei Patrick","lastName":"Kwok","suffix":""},{"id":450805441,"identity":"99254dd3-4c0e-42ac-909a-2051720ccfbc","order_by":16,"name":"Chia-Chin Lin","email":"","orcid":"","institution":"University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Chia-Chin","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-03-18 07:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6250414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6250414/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12912-025-03492-0","type":"published","date":"2025-07-17T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88506044,"identity":"6a05823c-799d-47c9-8150-ef89b66122dd","added_by":"auto","created_at":"2025-08-07 07:29:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1614241,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6250414/v1/eeb0d059-3741-42b0-a64b-2b1086af9486.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of generative artificial intelligence (GenAI) patient simulation on clinical competency among global nursing undergraduates: A cross-over randomised controlled trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe integration of artificial intelligence (AI) into healthcare has prompted a growing emphasis in the literature on the necessity of AI readiness among nursing students and professionals. AI readiness encompasses the knowledge, skills, and attitudes required for the effective application of AI in healthcare, including preventive measures, diagnostics, treatment, and comprehensive care delivery [1]. However, the potential for AI to perpetuate biases and adversely affect patient care has been noted, emphasizing the need for rigorous training and oversight [2]. To mitigate these risks, nursing curricula should proactively foster AI readiness through targeted education on AI fundamentals, its strengths and limitations, and ethical considerations. The literature supports curricular innovations like dedicated coursework and simulation-based learning with GenAI to build foundational AI readiness and reduce biases in healthcare [3, 4]. Further research is required to systematically assess various pedagogical approaches using AI and their long-term effects.\u003c/p\u003e\n"},{"header":"Background","content":"\u003cp\u003eThe increasing integration of artificial intelligence (AI) into healthcare has underscored the necessity for nursing students and professionals to develop AI readiness [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This readiness is crucial as AI technologies, including virtual reality (VR) and conversational agents, are becoming integral to nursing education, enhancing learning experiences by providing immersive and interactive environments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, 360\u0026deg; VR videos simulate health and social care scenarios that are typically inaccessible in traditional educational settings, thereby significantly improving communication skills and self-efficacy in AI-enabled simulations. However, there are concerns regarding AI's perceived human-like qualities, which could impact the authenticity of these simulations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBuilding on this foundation, pilot studies have demonstrated that integrating 360\u0026deg; VR with structured debriefing through learning management systems can enhance students' clinical competence and decision-making confidence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This approach not only fosters a deeper understanding of clinical scenarios but also prepares students for the complexities of AI-driven healthcare environments. The emergence of Generative AI (GenAI) patient simulation represents a novel trend in nursing education, necessitating targeted education on AI fundamentals, its strengths, limitations, and ethical considerations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe literature further emphasizes the importance of stakeholder engagement and a comprehensive understanding of AI's clinical role [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This includes the integration of coursework that addresses AI biases, ensuring that future nurses are equipped to navigate the ethical and practical challenges posed by AI technologies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While text-to-text GenAI tools like ChatGPT offer educational benefits, caution is advised due to potential risks such as AI hallucination, misinformation, and cultural sensitivity issues [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings advocate for urgent curricular reform to prepare nurses for AI practice, highlighting the need for a balanced approach that leverages AI's potential while mitigating its risks [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimulation-based GenAI learning has been shown to enhance AI readiness and reinforce core nursing concepts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Students exposed to GenAI-generated cases exhibit improved clinical reasoning compared to those trained through traditional methods [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This suggests that GenAI can play a pivotal role in preparing nursing students for real-world clinical challenges. However, further research is imperative to optimize the integration of GenAI into nursing education while ensuring that training remains bias-free.\u003c/p\u003e \u003cp\u003eIn summary, the adoption of AI in nursing education is accelerating, necessitating improved competency among students and faculty. Current literature supports curricular innovations such as dedicated coursework and GenAI simulations to build AI readiness and mitigate biases. Future research should systematically evaluate various AI pedagogical approaches and their long-term impacts, ensuring that nursing education evolves in tandem with technological advancements in healthcare.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis was a cross-over randomised controlled study with two intervention arms: GenAI and 360\u0026deg; VR simulation. The participants were allocated equally to the study arms.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Participants\u003c/h3\u003e\n\u003cp\u003e Forty-four undergraduate nursing students from Hong Kong, Taiwan, Thailand, Spain, South Korea, and Australia in years 1, 2, and 3 of their studies participated. Subgroups were created, each consisting of three undergraduate nursing students from various academic years. A total of 22 participants were randomly assigned to Group A (who received the 360\u0026deg; VR simulation first and GenAI second), and the other 22 participants were assigned to Group B (who received GenAI first and the 360\u0026deg; VR simulation second). The allocation process was randomised with a 1:1 allocation ratio. This sequence was generated by an independent researcher to ensure unbiased assignment and was concealed from both groups of participants and the researchers involved in the study. Moreover, all of the participants provided written informed consent before the study, and the voluntary nature of participation and the confidentiality of the data were strongly emphasised throughout the process. This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (IRB number: UW 24\u0026ndash;396).\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eThe participants participated in the simulation program over the course of three distinct days, followed by a washout period. Five questionnaire tools were used to collect data: the Clinical Competence Questionnaire (CCQ), the Cultural Awareness Scale (CAS), the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), the Simulation Effectiveness Tool \u0026ndash; Modified (SET-M), and a demographic questionnaire. The questionnaires were administered to the participants at three time points \u0026ndash; pre-intervention (baseline [T0]), time 1 (T1), and time 2 (cross-over session) (T2).\u003c/p\u003e\n\u003ch3\u003eCCQ\u003c/h3\u003e\n\u003cp\u003eThe 47-item CCQ, originally developed by Liou, was utilized in this study to assess participants\u0026rsquo; self-perceived clinical competency at both T0 and T1 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The questionnaire consists of four competency components, namely nursing professional behaviours (NP; 16 items, score range: 16\u0026ndash;90), general performance (GP; 13 items, score range: 13\u0026ndash;65), core nursing skills (CNS; 12 items, score range: 12\u0026ndash;60), and advanced nursing skills (ANS; 6 items, score range: 6\u0026ndash;30). The items are scored using a 5-point Likert scale (1 = \u0026lsquo;Do not have a clue\u0026rsquo;; 2 = \u0026lsquo;Know in theory, but not confident at all in practice\u0026rsquo;; 3 = \u0026lsquo;Know in theory, can perform some parts in practice independently, and need supervision to be readily available\u0026rsquo;; 4 = \u0026lsquo;Know in theory, competent in practice, need contactable sources of supervision\u0026rsquo;; 5 = \u0026lsquo;Know in theory, competent in practice without supervision\u0026rsquo;). The total score ranges from 47 to 235 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A higher score indicates higher perceived competence. Cronbach\u0026rsquo;s alpha for the CCQ was 0.98, with the reliability of each component ranging from 0.87 to 0.95 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCAS\u003c/h3\u003e\n\u003cp\u003eThe CAS was designed by Rew to measure the cultural awareness of nursing students [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The CAS is based on the pathway model and comprises 36 items divided into five subscales: general education experience, cognitive awareness of attitudes, classroom and clinical instruction, research issues, and clinical practice. The reliability was .91 for students and .82 for faculty. The subscales\u0026rsquo; reliability values ranged between .66 and .99 for students and between .56 and .87 for faculty [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMAIRS-MS\u003c/h2\u003e \u003cp\u003eThe MAIRS-MS instrument, initially created by Karaca, requires participants to rate their self-assessment on a 5-point Likert scale (ranging from 1 = \u0026lsquo;strongly disagree\u0026rsquo; to 5 = \u0026lsquo;strongly agree\u0026rsquo;) on 22 statements regarding their AI readiness [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The MAIRS-MS is subdivided into four factors: cognition (8 items), ability (8 items), vision (3 items), and ethics (3 items). Two exemplary items are \u0026lsquo;I can explain how AI systems are trained\u0026rsquo; (cognition) and \u0026lsquo;I can explain the limitations of AI technology\u0026rsquo; (vision) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The internal consistency of the overall scale to be acceptable (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.88), and the Cronbach\u0026rsquo;s alphas for the individual factors were 0.83 (cognition), 0.77 (ability), 0.72 (vision), and 0.63 (ethics) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSET-M\u003c/h3\u003e\n\u003cp\u003eThe participants\u0026rsquo; confidence was measured using the SET-M at T1 only. The modified version of this instrument was published in 2015, with a total of 19 items and a 3-point response scale [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The items are divided into four domains: pre-briefing, learning, confidence, and debriefing. Cronbach\u0026rsquo;s alpha for the overall SET-M was .94, with the reliability of each domain ranging from .83 to .91 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDemographic questionnaire\u003c/h3\u003e\n\u003cp\u003eA questionnaire was used to collect the participants\u0026rsquo; demographic data, including gender, year of study, and previous clinical experience.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntervention Details\u003c/h2\u003e \u003cp\u003eThe participants engaged in two distinct clinical scenarios: a pneumonia case at T1 and an appendicitis case at T2. For both interventions, the same clinical cases were utilised; however, they were approached differently to ensure a fair comparison of the educational outcomes. This design allowed for an evaluation of the effectiveness of each method in enhancing the nursing students\u0026rsquo; ability to prioritise nursing diagnoses, a critical skill in delivering high-quality patient care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComponents of GenAI Patient Simulation\u003c/h2\u003e \u003cp\u003eThe proposed GenAI Patient Simulation system for nursing education integrates several key components to enhance the learning experience. Firstly, it categorizes nursing diagnoses into distinct domains such as physiological, psychosocial, and cultural, further classifying them into actual, potential, and wellness diagnoses to provide a structured approach to patient care assessment. Secondly, the system incorporates Maslow\u0026rsquo;s hierarchy of needs, aligning nursing diagnoses with the five levels of human needs to prioritize care, thereby enriching students' theoretical understanding and guiding practical decision-making. Thirdly, it employs a standardized scale to assess the severity and complexity of disease conditions, considering factors like symptom acuity, potential for rapid deterioration, and intervention urgency, which aids students in identifying critical nursing diagnoses. Finally, the system establishes care priorities through a scoring mechanism that evaluates each diagnosis based on its impact on patient safety, potential complications, and overall outcomes, ensuring informed clinical decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImplementation of Constructive Feedback through GenAI debriefing\u003c/h2\u003e \u003cp\u003eThe implementation of constructive feedback through GenAI debriefing in the proposed system is designed to facilitate asynchronous learning by providing students with personalized, real-time feedback post-simulation. This process involves reflective practice where students analyze their responses, actions, and the outcomes of their clinical decisions. GenAI evaluates the rationale behind each nursing diagnosis, assessing the alignment with established grading frameworks to highlight areas of strength and improvement, thereby deepening students' understanding of prioritization. Additionally, it scrutinizes the actions taken during simulations, offering feedback on the appropriateness of interventions to enhance clinical judgment and critical thinking. Finally, GenAI assesses the results of these actions, providing insights into the effectiveness of interventions based on chosen diagnoses, reinforcing evidence-based practice. This debriefing not only offers expert feedback on the correct prioritization of nursing diagnoses but also generates a score based on the accuracy of assessments and choices, ensuring a comprehensive learning experience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplementation of 360\u0026deg; VR\u003c/h2\u003e \u003cp\u003eThe immersive 360\u0026deg; VR experience facilitated decision-making and interaction within the video by capturing the students\u0026rsquo; timestamps on a learning management system. The The 360\u0026deg; VR videos content was parallel to the GenAI patient simulation. Students participated in a debriefing session led by trained faculty members. This debriefing process enabled vicarious peer learning in which the students reviewed each other\u0026rsquo;s timestamps and comments.\u003c/p\u003e \u003cp\u003eImportantly, the platform\u0026rsquo;s structured debriefing feature provides guidance for educators, allowing them to conduct a systematic debriefing process. These functionalities ensure that both educators and students receive timely and relevant feedback in a structured manner. The realism of the scenarios presented in the VR videos ensures that the educational content remains aligned with the objectives of the intervention, thereby fostering a deeper understanding and retention of knowledge among students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, including means and standard deviations, were used to summarise the participants\u0026rsquo; demographics and scores on the CCQ, CAS, MAIRS-MS, and SET-M. The baseline characteristics of the intervention and control groups were compared using chi-square tests or Fisher\u0026rsquo;s exact tests for categorical data and independent t-tests for continuous data.\u003c/p\u003e \u003cp\u003eA linear mixed-model analysis was conducted to analyse the CCQ, CAS, and MAIRS-MS scores. This model treated the participants as a random effect and included time, group, randomisation sequence order, and the time\u0026ndash;group interaction as fixed effects [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Linear contrasts were used to assess both between-group differences and within-group changes over time. All of the statistical analyses were performed using IBM SPSS version 29.0 (IBM Corp., Armonk, NY, USA). Two-sided tests were used throughout, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\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\u003eBaseline Characteristic of the Participants (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eParticipants, No (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA group\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB group\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1 (1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.9 (1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you have any TUNS experience?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you attended any clinical practicum?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo you have any GenAI training before?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCQ Total Score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154.16 (32.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.23 (31.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151.09 (34.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNursing Professional Behaviors, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.25 (13.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.82 (13.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.68 (14.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Performance, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.50 (6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.68 (5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.32 (6.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore Nursing Skills, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.91 (11.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.18 (11.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.64 (11.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced Nursing Skills, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.50 (5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.55 (4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.45 (5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS Total Score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189.43 (40.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.91 (42.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.95 (38.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral Educational Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.75 (16.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.00 (17.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.50 (16.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.86 (8.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.05 (8.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.68 (8.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Issue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.77 (5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.41 (5.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.14 (5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehaviors or Comfort with Interactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.73 (8.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.36 (9.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.09 (8.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient Care or Clinical Issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.32 (7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.09 (7.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.55 (7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAISMS Total Score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.27 (14.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.00 (17.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.55 (11.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.30 (6.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.77 (7.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.82 (5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.77 (5.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.86 (6.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.68 (5.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.05 (2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.27 (2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.82 (2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.16 (2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.09 (2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.23 (2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.873\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\u003eIn total, 44 participants were included in the study, divided equally into two groups (A group: n\u0026thinsp;=\u0026thinsp;22; B group: n\u0026thinsp;=\u0026thinsp;22). The mean age of participants was 21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24 years, with no significant difference between groups (P\u0026thinsp;=\u0026thinsp;0.632). Most participants were female (84.1%), and the distribution of sex did not differ significantly between groups (P\u0026thinsp;=\u0026thinsp;0.216). Regarding the year of study, most participants were in Year 2 (34.1%), and there was no significant difference between groups (P\u0026thinsp;=\u0026thinsp;0.627).\u003c/p\u003e \u003cp\u003eParticipants with prior TUNS experience comprised 20.5%, while those without accounted for 79.5%, with no significant difference between groups (P\u0026thinsp;=\u0026thinsp;0.262). Similarly, the majority (73.6%) had attended clinical practicum, and this characteristic was evenly distributed across groups (P\u0026thinsp;=\u0026thinsp;1.000). Only a minority (18.2%) had GenAI training experience, with no significant difference between groups (P\u0026thinsp;=\u0026thinsp;0.240).\u003c/p\u003e \u003cp\u003eBaseline scores for CCQ Total Score, Nursing Professional Behaviors, General Performance, Core Nursing Skills, and Advanced Nursing Skills were comparable between groups, with P values ranging from 0.211 to 0.853, indicating no significant differences. The CAS Total Score also showed no significant difference between groups (P\u0026thinsp;=\u0026thinsp;0.810). These findings suggest that demographic and baseline clinical characteristics were well-balanced across the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEffects of the Interventions on CCQ (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\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\u003e\u003cb\u003eMixed effects analysis for the interventional effects (n\u0026thinsp;=\u0026thinsp;44)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003cp\u003eWithin-Group Change (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup B(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003cp\u003eWithin-Group Change (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBetween-Group Difference\u003c/p\u003e \u003cp\u003eMean (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCQ-Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.95 (13.96, 35.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.68 (36.68, 58.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.59 (2.77, 30.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.09 (20.09, 42.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.64 (28.64, 50.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.41 (-11.41, 16.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCQ-NP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.82 (3.70, 13.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.86 (6.74, 16.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.91 (-5.64, 9.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.50 (7.38, 17.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.59 (8.47, 18.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05 (-7.59, 7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCQ-GP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.36 (0.45, 6.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.41 (6.49, 12.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.68 (2.72, 8.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.73 (0.81, 6.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.77 (3.85, 9.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.68 (-0.28, 5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCQ-CNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.23 (5.06, 13.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.14 (13.96, 22.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.36 (2.08, 10.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.36 (5.19, 13.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.82 (9.65, 17.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.91 (-2.38, 6.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCQ-ANS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.55 (1.31, 5.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.27 (6.04, 10.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.64 (-0.39, 5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.50 (3.27, 7.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.45 (3.22, 7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.14 (-5.16, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS-Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.27 (0.22, 36.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.59 (9.54, 45.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.36 (-15.08, 27.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.32 (-2.73, 33.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.91 (2.86, 38.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.64 (-18.80, 24.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS-GEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.95 (-3.92, 11.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.41 (-2.46, 13.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.95 (-6.70, 12.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.32 (-4.55, 11.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50 (-5.37, 10.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68 (-8.97, 10.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS-CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.77 (1.02, 8.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.09 (2.33, 9.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.95 (-1.01, 6.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23 (0.47, 7.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.05 (2.29, 9.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.45 (-0.51, 7.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS-RI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68 (-0.08, 5.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.05 (-0.72, 4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09 (-3.17, 3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64 (-2.13, 3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50 (-1.26, 4.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.59 (-1.67, 4.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS-BOCWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.86 (-1.16, 6.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.59 (1.57, 9.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.55 (-5.43, 4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68 (-1.34, 6.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.00 (-0.03, 8.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.95 (-6.84, 2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS-PCOCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.00 (0.55, 7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.45 (5.01, 11.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.91 (-3.00, 4.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.45 (1.01, 7.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.86 (3.42, 10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.14 (-5.04, 2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAISMS-Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.64 (9.80, 23.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.18 (23.35, 37.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.09 (4.43, 19.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAISMS-Cognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.50 (1.46, 7.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.59 (8.55, 14.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.14 (2.89, 9.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAISMS-Ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00 (2.25, 9.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.36 (6.62, 14.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.18 (0.13, 8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAISMS-Vision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.23 (1.79, 4.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.55 (3.11, 5.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86 (-0.60, 2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAISMS-Ethics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.91 (1.63, 4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.68 (2.40, 4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.91 (-0.53, 2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eCCQ\u003c/b\u003e: Clinical Competence Questionnaire\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eNP\u003c/b\u003e: Nursing professional behaviors\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eGP\u003c/b\u003e: General Performance\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eCNS\u003c/b\u003e: Core Nursing Skills\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eANS\u003c/b\u003e: Advanced Nursing Skills\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eCAS\u003c/b\u003e: Cultural Awareness Scale\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eGEE\u003c/b\u003e: General Educational Experience\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eCA\u003c/b\u003e: Cognitive Awareness\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eRI\u003c/b\u003e: Research Issues\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003eBOCWI\u003c/b\u003e: Behavior or Comfort with Interactions\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e● \u003cb\u003ePCOCI\u003c/b\u003e: Patient Care or Clinical Issues\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of mixed-effect tests of the interventions\u0026rsquo; effects on clinical competence. The results indicate significant improvements across various measures for both groups. For the CCQ-Total, both Group A and B demonstrated notable within-group improvements at both T1 (Group A: 24.95, 95% CI [13.96, 35.95], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group B: 47.68, 95% CI [36.68, 58.68], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and T2 (Group A: 31.09, 95% CI [20.09, 42.09], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group B: 39.64, 95% CI [28.64, 50.64], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The between-group difference was statistically significant at T1 (16.59, 95% CI [2.77, 30.41], p\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e \u003cp\u003eIn the NP component, both groups showed significant improvements at both time points (Group A at T1: 8.82, 95% CI [3.70, 13.94], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group A at T2: 12.50, 95% CI [7.38, 17.62], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group B at T1: 11.86, 95% CI [6.74, 16.98], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group B at T2: 13.59, 95% CI [8.47, 18.71], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For the GP component, both Groups A and B showed significant improvements at T1 (Group A: 3.36, 95% CI [0.45, 6.28], p\u0026thinsp;=\u0026thinsp;0.024; Group B: 9.41, 95% CI [6.49, 12.33], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and T2 (Group A: 3.73, 95% CI [0.81, 6.65], p\u0026thinsp;=\u0026thinsp;0.013; Group B: 6.77, 95% CI [3.85, 9.69], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the CNS component, both Groups A and B showed significant improvements at T1 (Group A: 9.23, 95% CI [5.06, 13.40], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group B: 18.14, 95% CI [13.96, 22.31], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and T2 (Group A: 9.36, 95% CI [5.19, 13.54], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group B: 13.82, 95% CI [9.65, 17.99], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding the ANS component, both groups exhibited significant improvements at both time points (Group A at T1: 3.55, 95% CI [1.31, 5.78], p\u0026thinsp;=\u0026thinsp;0.002; Group A at T2: 8.27, 95% CI [6.04, 10.51], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group B at T1: 5.50, 95% CI [3.27, 7.73], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Group B at T2: 5.45, 95% CI [3.22, 7.69], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the between-group differences were significant at T1 for the GP component (5.68, 95% CI [2.72, 8.64], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CNS component (6.36, 95% CI [2.08, 10.65], p\u0026thinsp;=\u0026thinsp;0.005), with Group B demonstrating substantially higher scores than Group A in these domains (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEffects of the Interventions on CAS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of mixed-effect tests of the interventions\u0026rsquo; effects on cultural awareness. In terms of the total score, Group A improved significantly at T1 (18.27, 95% CI [0.22, 36.32], p\u0026thinsp;=\u0026thinsp;0.047) and marginally significantly at T2 (15.32, 95% CI [-2.73, 33.37], p\u0026thinsp;=\u0026thinsp;0.095), and Group B improved significantly both at T1 (27.59, 95% CI [9.54, 45.64], p\u0026thinsp;=\u0026thinsp;0.003) and T2 (20.91, 95% CI [2.86, 38.96], p\u0026thinsp;=\u0026thinsp;0.024).\u003c/p\u003e \u003cp\u003eBoth groups demonstrated significant improvements in the Cognitive Awareness measure at both T1 and T2 (Group A at T1: 4.77, 95% CI [1.02, 8.53], p\u0026thinsp;=\u0026thinsp;0.013; Group A at T2: 4.23, 95% CI [0.47, 7.98], p\u0026thinsp;=\u0026thinsp;0.028; Group B at T1: 6.09, 95% CI [2.33, 9.85], p\u0026thinsp;=\u0026thinsp;0.002; Group B at T2: 6.05, 95% CI [2.29, 9.80], p\u0026thinsp;=\u0026thinsp;0.002). For the Patient Care or Clinical Issues measure, both Group A and Group B showed significant improvements at T1 (Group A: 4.00, 95% CI [0.55, 7.45], p\u0026thinsp;=\u0026thinsp;0.023; Group B: 8.45, 95% CI [5.01, 11.90], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and T2 (Group A: 4.45, 95% CI [1.01, 7.90], p\u0026thinsp;=\u0026thinsp;0.012; Group B: 6.86, 95% CI [3.42, 10.31], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No significant within-group changes or between-group differences were observed for the General Educational Experience, Research Issue and Behavior, or Comfort with Interactions measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEffects of the Interventions on MAIRS-MS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of mixed-effect tests of the interventions\u0026rsquo; effects on the MAIRS-MS. In the MAIRS-MS Total Score, Group A exhibited significant changes from T0 to T1 with an increase of 16.64 (95% CI [9.80, 23.47], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Group B showed a significant change from T0 to T1 with an increase of 30.18 (95% CI [23.35, 37.01], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A significant between-group difference was noted (12.09, 95% CI [4.43, 19.75], p\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll of the other subscales, namely Cognition, Ability, Vision, and Ethics, demonstrated significant increases, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Significant between-group differences were observed in Cognition and Ability. Overall, both groups demonstrated significant improvements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEffect of the Interventions on SET-M (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimulation Effectiveness Tool-Modified (SET‐M) responses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimulation Effectiveness Tool-Modified (SET‐M) responses\u003c/p\u003e \u003cp\u003eAll respondents (N\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvey questions:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly Agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSomewhat Agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDo Not Agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrebriefing subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrebriefing increased my confidence.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.3% (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.7% (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrebriefing was beneficial to my learning.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.9% (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.1% (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am better prepared to respond to changes in my patient\u0026rsquo;s condition.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.5% (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.5% (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI developed a better understanding of the pathophysiology.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2% (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am more confident of my nursing assessment skills.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2% (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI felt empowered to make clinical decisions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.3% (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7% (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI developed a better understanding of medications.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.9% (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3% (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI had the opportunity to practice my clinical decision making skills.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.5% (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.5% (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am more confident in my ability to prioritize care and interventions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2% (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am more confident in communicating with my patient.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.7% (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3% (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am more confident in my ability to teach patients about their illness and interventions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.5% (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.5% (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am more confident in my ability to report information to health care team.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2% (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am more confident in providing interventions that foster patient safety.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.0% (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0% (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am more confident in using evidence-based practice to provide nursing care.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.7% (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.3% (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDebriefing subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDebriefing contributed to my learning.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.0% (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0% (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDebriefing allowed me to verbalize my feelings before focusing on the scenario.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2% (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDebriefing was valuable in helping me improve my clinical judgment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2% (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDebriefing provided opportunities to self-reflect on my performance during simulation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.2% (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDebriefing was a constructive evaluation of the simulation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.0% (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0% (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0% (0)\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the responses of the participants to the SET-M. Most of the participants (75%, n\u0026thinsp;=\u0026thinsp;33) strongly agreed that debriefing benefited their learning. Regarding learning confidence, over three quarters of the participants (77.3%, n\u0026thinsp;=\u0026thinsp;43) strongly agreed that their nursing assessment skills had improved. Additionally, most of the participants (75.0%, n\u0026thinsp;=\u0026thinsp;33) strongly agreed that they were confident in performing handovers to the healthcare team and using evidence-based practice to provide nursing care (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the impact of two innovative interventions \u0026ndash; GenAI and 360\u0026deg; VR simulation \u0026ndash; on clinical competence, cultural awareness, and AI readiness among nursing students in two distinct groups. Group A received 360\u0026deg; VR Simulation first, followed by GenAI, while Group B experienced these interventions in the reverse order. Both groups demonstrated significant improvements in clinical competence, with Group B initially showing advantages in general performance and core nursing skills. Regarding cultural awareness, Group B made earlier gains in educational experience and cognitive awareness, although Group A eventually matched these improvements. Notably, both groups exhibited substantial progress in AI readiness across all subscales. Additionally, a majority of students reported positive experiences with debriefing, contributing to increased confidence in their nursing skills. Both 360\u0026deg; VR simulation and GenAI patient simulation are therefore effective pedagogical strategies for enhancing the clinical outcomes of nursing students. However, GenAI evidently demonstrates a more pronounced impact, as its initial implementation tends to engage students more effectively in the learning process, leading to improved educational results. Specifically, when GenAI patient simulation is provided first, it yields greater outcomes; subsequently utilising 360\u0026deg; VR can enhance these results.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eClinical Competence\u003c/h2\u003e \u003cp\u003eThe results of the study indicate that the intervention enhanced clinical competence in both groups, with Group B demonstrating a more pronounced initial improvement at T1, particularly in the General Practice (GP) and Clinical Nursing Skills (CNS) components of the Clinical Competence Questionnaire (CCQ). However, by T2, Group A had begun to catch up, suggesting that while their progress was initially slower, they eventually reached comparable levels of competence. This implies that the intervention had a more gradual impact on some participants but was ultimately effective for both groups over time. The customisation enabled by GenAI interventions, which catered to individual learners' needs, preferences, and learning styles, likely facilitated targeted feedback, adaptive challenges, and content alignment with specific clinical roles and responsibilities. By providing tailored learning experiences, GenAI effectively supported the development of essential clinical competencies in both general practice and specialised nursing fields [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, GenAI interventions specifically targeted cognitive skills such as problem-solving, critical thinking, decision-making, and clinical reasoning, which are crucial for effective practice in healthcare settings [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Engaging participants in higher-order thinking tasks and complex scenarios relevant to their practice areas potentially enhanced the development of these cognitive skills more effectively than traditional interventions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA notable feature of GenAI is its ability to deliver real-time feedback, debriefing, performance monitoring, and adaptive learning pathways tailored to learners' responses and progress. This immediate feedback loop allows participants to identify areas for improvement, adjust their strategies, and monitor their growth over time, fostering an environment conducive to continuous learning and skill refinement. The personalised feedback and adaptive nature of GenAI likely contributed to the improved performance outcomes observed in the GP and CNS assessments of the CCQ. By leveraging advanced AI technologies, including deep learning and natural language processing, GenAI creates dynamic, responsive, and intelligent learning environments that optimise educational experiences, customise content delivery, and support individualised learning pathways focused on the specific competencies required for diverse nursing specialties [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eCultural Awareness\u003c/h2\u003e \u003cp\u003eThe interventions aimed at enhancing cultural awareness, as measured by the Cultural Awareness Scale (CAS), demonstrated significant improvements in both groups, particularly in the dimensions of Cognitive Awareness and Patient Care or Clinical Issues. Group B exhibited more substantial enhancements at both time points compared to Group A, indicating a potentially more effective intervention for this group. Nonetheless, both groups experienced significant gains in several core areas of cultural competence.\u003c/p\u003e \u003cp\u003eThe notable advancements in Cognitive Awareness and Patient Care or Clinical Issues underscore the interventions' efficacy in improving participants' understanding of cultural issues and their practical application in clinical settings. This is particularly crucial in healthcare, where cultural competence is increasingly recognized as vital for delivering high-quality, patient-centered care [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The balanced representation of students from various countries in both groups facilitated an increase in cultural awareness through GenAI patient simulations. These simulations were designed to interact according to distinct cultural beliefs and express cultural requirements, allowing students to engage in and gain insights from culturally nuanced exchanges, thereby enhancing their comprehension of cultural diversity within healthcare environments. The improvements in these areas suggest that the intervention successfully addressed key components of cultural competence, potentially leading to better patient outcomes and more culturally sensitive care practices.\u003c/p\u003e \u003cp\u003eHowever, the absence of significant changes in the areas of Behavior or Comfort with Interactions and Research Issues across both groups highlights a critical gap in the interventions. These components of cultural competence, which involve interpersonal dynamics and behavior in diverse settings, may necessitate more teaching sessions, such as role-playing, simulations, or real-world applications, to cultivate practical skills in navigating cultural interactions. This suggests that while cognitive and contextual aspects of cultural awareness can be effectively enhanced through the existing interventions, relational and behavioral dimensions might require more intensive or prolonged engagement to produce measurable improvements [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings indicate that interventions focusing on cultural awareness can significantly enhance healthcare professionals' cognitive understanding and clinical application of cultural competence. Such improvements are essential for fostering culturally sensitive healthcare environments, particularly in increasingly diverse patient populations. However, to achieve broader and more comprehensive cultural competence, future training programs should incorporate more specific elements related to research issues and comfort with intercultural interactions, areas that were not significantly impacted in this study.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eAI Readiness\u003c/h2\u003e \u003cp\u003eThe findings indicate that the intervention was effective in enhancing AI readiness for both groups, with Group B demonstrating a higher score than Group A. The significant improvements observed across all subscales of the MAIRS-MS suggest that the interventions were highly effective in increasing healthcare professionals' readiness to engage with and utilize AI in their practice. The notable between-group differences across all subscales imply that while both interventions were effective, Group B may have benefited from additional resources or a more conducive learning environment, resulting in greater gains. These results emphasize the importance of tailored interventions that can address varying levels of baseline readiness among healthcare professionals.\u003c/p\u003e \u003cp\u003eThe results indicate that both interventions successfully addressed key aspects of AI readiness, including cognitive understanding of AI concepts, the ability to interact with AI systems, vision for AI integration in healthcare, and ethical considerations surrounding AI use. The substantial increases in scores, particularly in the Cognition and Ability subscales, highlight that the participants gained theoretical knowledge about AI and practical skills in working with AI technologies. This comprehensive improvement across all dimensions of AI readiness is crucial for the successful implementation of AI in healthcare settings [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003ePerceived Simulation Effectiveness (SET-M)\u003c/h2\u003e \u003cp\u003e The analysis of the SET-M responses indicates that a majority of the participants perceived debriefing as highly beneficial to their learning, with 75% expressing strong agreement. Furthermore, a large majority of the participants reported increased confidence in their nursing assessment skills (77.3%) and handover performance (75.0%). These findings underscore the importance of perceived competence and confidence in clinical training, suggesting that structured peer interactions may enhance learning experiences and foster a supportive educational environment.\u003c/p\u003e \u003cp\u003eFuture research should investigate the long-term effects of these interventions on clinical competence through various methodologies. Longitudinal studies could provide insights into the sustainability of improvements over time, and comparative effectiveness research might help determine the relative impact of GenAI versus traditional educational methods. Qualitative inquiries into student experiences and perceptions could enrich our understanding of the mechanisms underlying the observed enhancements. Additionally, exploring a broader range of outcome measures, including patient care outcomes and the transferability of skills to real-world clinical settings, could inform future research efforts aimed at refining nursing education and practice. By addressing these areas, we can better understand how innovative educational interventions can shape the future of nursing training and ultimately improve patient care.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results provide robust evidence for the effectiveness of this cutting-edge approach in improving perceived clinical competence, cultural awareness, and AI readiness, with statistically significant improvements observed, particularly within Group B. Both 360\u0026deg; VR simulation and GenAI patient simulation were demonstrated as effective pedagogical strategies for enhancing the clinical outcomes of nursing students. However, GenAI exhibited a more significant impact, as its initial implementation engaged students more effectively in the learning process, thereby facilitating enhanced educational outcomes. Specifically, when GenAI patient simulation was introduced first, it produced superior results; the subsequent use of 360\u0026deg; VR amplified these benefits.\u003c/p\u003e \u003cp\u003eBoth groups exhibited marked improvements across all assessed domains, underscoring the intervention\u0026rsquo;s efficacy in clinical training. The favourable feedback regarding the SET-M further underscores that the participating students not only recognised the benefits of these GenAI simulations but also valued the function of GenAI debriefing, which enriched their educational experiences and facilitated their professional development.\u003c/p\u003e \u003cp\u003eThe integration of GenAI into patient simulations facilitates dynamic and adaptive learning environments, enabling learners to engage with realistic scenarios that reflect the complexities of actual clinical encounters. Through real-time feedback and GenAI debriefing, this technology improves diagnostic accuracy and supports critical skills such as decision-making and cultural competence, which are essential elements in today\u0026rsquo;s diverse healthcare landscape.\u003c/p\u003e \u003cp\u003eTo fully capitalise on these promising results, future research should concentrate on identifying the specific components of our GenAI intervention that drive these positive outcomes. By doing so, we can enhance educational strategies in clinical training, ensuring that healthcare professionals are not only technically proficient but also culturally aware and prepared for the challenges of contemporary medical practice. This exploration will be crucial in advancing healthcare education through innovative simulation methodologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eapproval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Ethical approval for this study was obtained from the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (IRB number: UW 24-396). Participants were ensured of data confidentiality and voluntariness of participation in and withdrawal from the study and personal written informed consent was obtained from each of them.\u0026nbsp;\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\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the present 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 no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the\u0026nbsp;HKU Teaching Development Grant\u0026nbsp;(Grant no.: 966). The funders did not have a role in study design, data collection, analysis, reporting or the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJohn Fung Tai Chun: Conceptualization, Methodology, Supervision, Writing- original draft and review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSiu Ling Chan: Conceptualization, Methodology, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eChoi Fung Lam: Methodology, Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eChung Yan Lam: Methodology, Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eChristopher Chi Wai Cheng:\u0026nbsp;Methodology, Formal analysis, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eMan Hin Lai:\u0026nbsp;Methodology, Formal analysis, Project administration, Data Curation, Visualization, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eCheuk Chun Joseph Ho: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAu Siu Lun:\u0026nbsp;Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eMak Lok Yi: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eSophie Hu: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eSupapak Phetrasuwan: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eJumpee Granger: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eJung Min Yoon: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eGulzar Mailk: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eClara Cabrera Moreno: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003ePatrick Kwok Man Hei: Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eChia-Chin Lin\u0026nbsp;(corresponding author): Project administration, Writing- review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the nursing students participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eTai Chun John Fung\u003csup\u003e\u0026nbsp;a*\u003c/sup\u003e, Siu Ling Chan\u0026nbsp;\u003csup\u003eb\u003c/sup\u003e,\u0026nbsp;Choi Fung Lam\u0026nbsp;\u003csup\u003ec\u003c/sup\u003e,\u0026nbsp;Chung Yan Lam\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003ed\u003c/sup\u003e,\u0026nbsp;Christopher Chi Wai Cheng\u0026nbsp;\u003csup\u003ee\u003c/sup\u003e,\u0026nbsp;Man Hin Lai\u0026nbsp;\u003csup\u003ef\u003c/sup\u003e,\u0026nbsp;Chun Joseph Ho Cheuk\u003csup\u003e\u0026nbsp;g\u003c/sup\u003e, Siu Lun Au\u003csup\u003e\u0026nbsp;h\u003c/sup\u003e, Lok Yi Mak \u003csup\u003ei\u003c/sup\u003e, Sophie Hu\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003ej\u003c/sup\u003e,\u0026nbsp;Supapak Phetrasuwan\u0026nbsp;\u003csup\u003ek\u003c/sup\u003e,\u0026nbsp;Jumpee Granger\u0026nbsp;\u003csup\u003el\u003c/sup\u003e, Jung Min Yoon \u003csup\u003em\u003c/sup\u003e, Gulzar Mailk \u003csup\u003en\u003c/sup\u003e, Clara Cabrera Moreno\u003csup\u003e\u0026nbsp;o\u003c/sup\u003e, Man Hei Patrick Kwok\u0026nbsp;\u003csup\u003ep\u003c/sup\u003e,\u0026nbsp;Chia-Chin Lin (corresponding author)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003eq*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea, b, c, d, f, g, h, p, q\u0026nbsp;\u003c/sup\u003e \u003cem\u003eUniversity of Hong Kong, Hong Kong\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u0026nbsp;\u003c/sup\u003e\u003cem\u003eThe Chinese University of Hong Kong, Hong Kong\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ei\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Hong Kong Baptist University, China\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ej\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;National Yang Ming Chiao Tung University, Taiwan\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ek\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Mahidol University, Thailand\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003el\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Ramathibodi School of Nursing, Thailand\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003em\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;EWHA University, Korea, Democratic People\u0026apos;s Republic of\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003en\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;La Trobe University, Australia\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003eo\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;University of Navarra, Spain\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e* Corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFung Tai Chun John \u0026amp; Chia-Chin Lin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u0026rsquo;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eemail address:\u0026nbsp;\u003c/strong\u003e\u003cstrong\[email protected]\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026amp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\[email protected]\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLaupichler, M. 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(2007). \u003cem\u003eLinear and generalized linear mixed models and their applications\u003c/em\u003e (Vol. 1). New York: Springer.\u003c/li\u003e\n\u003cli\u003eZhang, P., \u0026amp; Kamel Boulos, M. N. (2023). Generative AI in medicine and healthcare: Promises, opportunities and challenges. \u003cem\u003eFuture Internet\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(9), 286. \u003c/li\u003e\n\u003cli\u003eRony, M. K. K., Parvin, M. R., \u0026amp; Ferdousi, S. (2024). Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. \u003cem\u003eNursing Open\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eGlauberman, G., Ito-Fujita, A., Katz, S., \u0026amp; Callahan, J. (2023). Artificial intelligence in nursing education: Opportunities and challenges. \u003cem\u003eHawai\u0026rsquo;i Journal of Health \u0026amp; Social Welfare\u003c/em\u003e, 82(12), 302.\u003c/li\u003e\n\u003cli\u003eConrad, E. J., \u0026amp; Hall, K. C. (2024). Leveraging generative AI to elevate curriculum design and pedagogy in public health and health promotion. \u003cem\u003ePedagogy in Health Promotion\u003c/em\u003e, 23733799241232641.\u003c/li\u003e\n\u003cli\u003eChae, D., Kim, J., Kim, S., Lee, J., \u0026amp; Park, S. (2020). Effectiveness of cultural competence educational interventions on health professionals and patient outcomes: A systematic review. \u003cem\u003eJapan Journal of Nursing Science\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(3), e12326.\u003c/li\u003e\n\u003cli\u003eWalkowska, A., Przymuszała, P., Marciniak-Stępak, P., Nowosadko, M., \u0026amp; Baum, E. (2023). Enhancing cross-cultural competence of medical and healthcare students with the use of simulated patients\u0026mdash;A systematic review. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(3), 2505.\u003c/li\u003e\n\u003cli\u003eHamad, M., Qtaishat, F., Mhairat, E., Al-Qunbar, A., Jaradat, M., Mousa, A., ... \u0026amp; Alkhaldi, S. (2024). Artificial intelligence readiness among Jordanian medical students: Using Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). \u003cem\u003eJournal of Medical Education and Curricular Development\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 23821205241281648.\u003c/li\u003e\n\u003cli\u003eSorte, S. R., Rawekar, A., \u0026amp; Rathod, S. B. (2024). Understanding AI in healthcare: Perspectives of future healthcare professionals. \u003cem\u003eCureus\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(8).\u003c/li\u003e\n\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, Patient simulation, Clinical reasoning, 360-degree virtual reality, Clinical competence, Medical language, Nursing education, Randomised controlled trial","lastPublishedDoi":"10.21203/rs.3.rs-6250414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6250414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aims\u003c/h2\u003e \u003cp\u003eClinical competency is paramount for nurses to ensure that patients receive safe, high-quality care. Generative artificial intelligence (GenAI) in nursing education is gaining attention, and evidence shows its suitability for real-life situations. GenAI may be an effective solution for enhancing nurses\u0026rsquo; clinical competency. This study compared the impact of scenario-based GenAI patient simulation versus immersive 360\u0026deg; virtual reality (VR) simulation on educational outcomes, namely clinical competence, cultural awareness, AI readiness, and simulation effectiveness.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-over randomised controlled study design was conducted from June 2024 to August 2024. Forty-four undergraduate nursing students in years 1, 2, and 3 were selected to participate. Subgroups were formed, each comprising three undergraduate nursing students from different years. They were randomised to receive either a GenAI patient simulation (intervention, Group B) or 360\u0026deg; VR simulation (control, Group A) for three separate days and with a washout period. Four self-reported questionnaires were used to measure clinical competency: the Clinical Competence Questionnaire (CCQ), Cultural Awareness Scale (CAS), Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), and Simulation Effectiveness Tool \u0026ndash; Modified Questionnaire (SET-M).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study revealed notable improvements in clinical competence and confidence among the participants. Group A demonstrated significant enhancements in the CCQ at both time points, and Group B also showed meaningful progress. Both groups experienced changes in the CAS-Total scores, although these changes were not statistically significant. In terms of the MAIRS-MS total score, Group A had a significant increase at time 1 (T1), and Group B showed an improvement from baseline to time 2 (cross-over session, T2). Regarding SET-M results, most participants (75%) felt that debriefing contributed to their learning, and 77.3% reported increased confidence in their nursing assessment skills.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings offer compelling evidence of its effectiveness in enhancing clinical outcomes, as assessed by the CCQ, CAS, and MAIRS-MS. Importantly, our results reveal statistically significant improvements in these measures, particularly within Group B. Both 360\u0026deg; VR simulation and GenAI patient simulation with real-time feedback and GenAI debriefing can serve as powerful teaching tools for improving nursing students\u0026rsquo; clinical outcomes; however, GenAI exhibits a notably greater effect.\u003c/p\u003e\u003ch2\u003eClinical trial registration/number\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e","manuscriptTitle":"Effects of generative artificial intelligence (GenAI) patient simulation on clinical competency among global nursing undergraduates: A cross-over randomised controlled trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 16:47:32","doi":"10.21203/rs.3.rs-6250414/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-28T08:45:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T12:37:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-22T01:27:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-21T14:55:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174095287991675812700812053164884885094","date":"2025-05-07T16:45:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306754498849338813192919625403673964938","date":"2025-05-02T01:46:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253312474542642183761711603601336048374","date":"2025-05-01T01:35:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172890855742977418445729032041326646337","date":"2025-05-01T00:53:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30564545881748778532133255977543912183","date":"2025-04-30T12:38:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40885107090085797390067491332766615864","date":"2025-04-28T10:34:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T07:38:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-25T09:13:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T07:58:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-04-25T07:57:18+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":"487743f4-0e26-4049-92d2-788847a8dcd8","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T07:11:15+00:00","versionOfRecord":{"articleIdentity":"rs-6250414","link":"https://doi.org/10.1186/s12912-025-03492-0","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2025-07-17 15:57:03","publishedOnDateReadable":"July 17th, 2025"},"versionCreatedAt":"2025-05-02 16:47:32","video":"","vorDoi":"10.1186/s12912-025-03492-0","vorDoiUrl":"https://doi.org/10.1186/s12912-025-03492-0","workflowStages":[]},"version":"v1","identity":"rs-6250414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6250414","identity":"rs-6250414","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}