Construction of a faculty competency model for medical simulation education integrated with GenAI: A mixed study based on the perspective of medical students

Construction of a faculty competency model for medical simulation education integrated with GenAI: A mixed study based on the perspective of medical students | 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 Construction of a faculty competency model for medical simulation education integrated with GenAI: A mixed study based on the perspective of medical students Ying Zhuge, Xintong Yao, Hongxia Mei, Hongxiang Yao, qi Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6595296/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: The rapid advancement of Generative Artificial Intelligence (GenAI) is reshaping the landscape of medical simulation education, necessitating the enhancement of faculty competencies to effectively integrate evolving knowledge systems with GenAI for collaborative decision-making. However, current educational technologies face systemic limitations, including fragmented functionality, a disconnect between conventional and GenAI-driven teaching approaches, and a lack of dynamic capability assessment tools. Constructing a standardized capability scale is crucial to overcoming adaptation bottlenecks. Methods: Grounded in the integrated Technological Pedagogical Content Knowledge (TPACK) framework, this study employed a two-round Delphi method to construct a faculty competency assessment scale, with stratified sampling involving 434 participants from clinical medicine, medical technology, and nursing fields. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to test model fit (CFI=0.939, RMSEA=0.117). This study was not registered as a clinical trail. Result： The resulting 16-item scale exhibited strong psychometric properties, demonstrating excellent internal consistency (Cronbach’s α = 0.979), sampling adequacy (KMO = 0.961), and significant sphericity (Bartlett’s test, p < 0.001). No factor covariance was detected, and item consensus was high (Kendall’s W = 0.761, p < 0.001). The model showed acceptable fit (χ²/df = 6.893). Conclusion： This validated and standardized assessment model offers a robust empirical tool to support the integration of GenAI into simulation-based medical education. It provides a foundational framework for advancing faculty competency development, thereby fostering adaptive, technology-enhanced teaching practices in the era of intelligent education. Trial registration: This study was approved by the Institutional Ethics Committee of Wenzhou Medical University Second Affiliated Hospital, with Ethical Approval Number: XY-2024-003. Data collection was conducted between February 14 and March 28, 2025. TPACK competency model Generative Artificial Intelligence medical simulation education faculty competence mixed methods research Figures Figure 1 Figure 2 Figure 3 1. Background Generative Artificial Intelligence (GenAI) is fundamentally reshaping the landscape of medical simulation education by dynamically integrating clinical guidelines, evidence-based resources, and multimodal case data into a continuously evolving knowledge network Bland (2025);Janumpally, Nanua, Ngo, & Youens (2024a). This transformation is not merely technical—it redefines pedagogical roles, fostering a triadic model of collaborative decision-making among students, AI, and educatorsCheung et al. (2024). As a result, simulation educators must transition from traditional "knowledge authorities" to "knowledge navigators," requiring advanced competencies in critically screening, validating, and adapting AI-generated content throughout the technology adoption curve (honeymoon, frustration, adaptation, and acceptance phasesCheung et al. (2024). However, existing studies primarily explore GenAI through fragmented lenses—such as isolated applications (e.g., virtual case generation) or basic teacher training workshops(like AI tool workshops)—without addressing the need for systemic integration of GenAI into the broader framework of simulation-based medical educationGrunhut, Wyatt, & Marques (2021). Two major gaps persist: first, a disproportionate emphasis on technical functionalities at the expense of holistic pedagogical transformationGrunhut, Wyatt, & Marques (2021); second, the absence of dynamic, learner-centered analyses that reflect the evolving cognitive needs of students and the resulting generational shift in educational expectationsGrunhut, Marques, & Wyatt (2022). This misalignment hampers the effectiveness of current faculty development strategies in supporting sustainable, AI-driven transformation. To address these challenges, this study proposes the construction of a standardized faculty competency model tailored to GenAI-integrated medical simulation education. By deconstructing the synergistic relationship between traditional teaching competencies and GenAI-specific capabilitiesGrunhut, Marques, & Wyatt (2022), it aims to provide a validated empirical foundation to guide faculty development and institutional reform in the intelligent era. 2. Methods 2.1. Aim This study aims to construct and validate a competency model integrating GenAI with traditional instructional competencies for medical simulation educators. 2.2. Study design A mixed-methods approach was employed, incorporating the Delphi techniqueHong et al. (2019) and a cross-sectional survey for empirical validation. Details of the survey questionnaire are provided in Supplemental material. 2.3. Scale development and validation 2.3.1. Technological Pedagogical Content Knowledge (TPACK) The competency assessment scale was developed based on the Technological Pedagogical Content Knowledge (TPACK) framework, which emphasizes the dynamic interaction among content knowledge (CK), pedagogical knowledge (PK), and technological knowledge, serving as a foundational model for faculty competency development in the information ageAit Ali et al. (2023). The scale included two primary dimensions: (1) Traditional Teaching Competence (9 items), grounded in the integration of CK and PK, focused on core pedagogical abilities in medical simulation education. Items such as "Clinical Case Teaching Design Ability" reflected Shulman’s 6 concept of pedagogical content knowledge, emphasizing the alignment of teaching strategies with disciplinary content. (2) GenAI Integration Ability (7 items), designed in accordance with TPACK’s principle of contextual technology adaptation, assessed educators’ capacity to align GenAI tools with specific simulation scenarios. Items such as "Dynamic Matching of Technical Tools and Simulation Scenarios" and "Information Reliability Assessment" addressed ethical considerations and instructional alignment, ensuring that technology use enhances rather than overshadows pedagogy, and avoiding the risk of tool alienation. The item development adhered to TPACK’s context-sensitive and ethically aware principles, incorporating elements such as cross-disciplinary collaboration and information credibility to address challenges specific to GenAI integration in medical training. 2.3.2. Delphi Method design and verification To ensure theoretical rigor and practical relevance,experts were required to meet both of the following criteria: (1) at least 10 years of clinical teaching experience, and (2) demonstrated familiarity with the TPACK framework. In the first round of the Delphi process, five experts participated, including three clinical medicine specialists and two medical education scholars. All held senior professional titles and had extensive experience in simulation-based teaching and faculty development. To ensure balanced representation of the “technology–pedagogy–content” triad emphasized in the TPACK model, the content validity standard proposed by Beck (S-CVI/Ave ≥ 0.90) was employedPolit & Beck (2006). This rigorous threshold helped prevent undue emphasis on technical functionality during item formulation and promoted alignment with educational theory. Following distribution of the initial questionnaire, expert agreement was evaluated using Kendall’s coefficient of concordance, revealing a high level of consensus (W = 0.83, χ² = 53.12, df = 4, p < 0.001). Based on qualitative feedback, several items were revised for improved clarity and operational precision—for example, clearer definitions were provided for “teamwork competence” and “GenAI information discernment ability.” In the second round of consultation, the revised scale demonstrated even stronger expert consensus (W = 0.84, χ² = 53.50, df = 4, p < 0.001), indicating increasing convergence toward a theoretically coherent and practically relevant instrument. To further verify content validity, a validation panel of 10 participants was assembled, including five experts in medical simulation education (three clinical educators and two medical education researchers, all with senior professional titles and over 10 years of experience) and five learners (three senior undergraduate interns and two resident physicians), each with exposure to more than 20 simulation-based training sessions. Using a four-point Likert scale (1 = not relevant, 4 = highly relevant), item-level content validity indices (I-CVI) ranged from 0.70 to 1.00. At the scale level, both the universal agreement index (S-CVI/UA = 0.81) and the average agreement index (S-CVI/Ave = 0.96) met the quality benchmarks recommended by Polit and BeckPolit & Beck (2006), confirming the strong content validity of the scale, indicating that the revised items more accurately reflected both technological integration and pedagogical expectations in medical simulation contexts. 2.3.3. preliminary experiment design and verification Participants were randomly selected from individuals who had completed at least ten sessions of medical simulation-based training. To ensure immediacy and contextual relevance, questionnaires were distributed within 30 minutes after the course (Supplemental material). All 30 responses were valid, yielding a 100% effective response rate. Sample characteristics are detailed in Table 1. The assessment instrument employed a five-point Likert scale (1 = very unimportant, 5 = very important), with total scores ranging from 16 to 80. Based on principal component analysis, two latent factors were extracted, jointly explaining 69.086% of the total variance (Figure 1). The first factor, representing traditional medical simulation teaching competency, accounted for 38.674% of the variance and featured high loadings on items such as "Communication Skills" (loading = 0.862) and "Course Design and Organization Skills" (loading = 0.859). The second factor, reflecting the integration and innovation of GenAI technology in medical simulation education, accounted for 30.412% of the variance, with strong item loadings such as "GenAI Ethics and Safety Knowledge" (loading = 0.863) and "Ability to Guide Students in Using GenAI for Learning" (loading = 0.818). Content validity was established through reference to theoretical framework for integrated medical educationPolit & Beck (2006), ensuring comprehensive coverage of both pedagogical and technological domains. The overall internal consistency of the scale was excellent, with a Cronbach’s α coefficient of 0.932. Reliability for each dimension was also high, with α = 0.948 for the traditional teaching competency domain and α = 0.900 for the GenAI integration domain. The "GenAI integrated medical simulation education faculty competency needs table" wad shown in Table 2. 2.4. Sampling and evaluation Sample size estimation using G*Power 3.1 software determined that a minimum of 418 participants was required for adequate statistical power (Table 3 and Table 4). Participants were drawn from professional healthcare personnel who had attended at least ten medical simulation sessions between February 14 and March 28, 2025, at a tertiary hospital’s Clinical Skills Training Center (Ethical Approval No. XY-2024-003). All data were de-identified prior to analysis. Proportional stratified sampling was implemented based on historical training participation rates across three professional categories: clinical medicine, nursing, and medical technology (approximate ratio 6:1.5:1.3). Attendance records (N=1286) served as the sampling frame, including job type (e.g., interns or professionals within one year post-graduation). Within each stratum, subjects were assigned unique identification codes. Random sampling was conducted using Excel’s RANDBETWEEN function, with duplicates removed and additional draws performed as needed to meet target quotas (Table 5). 2.5. Data analysis Descriptive statistics were used to summarize demographic characteristics and item responses. Internal consistency was evaluated using Cronbach’s α coefficientTavakol & Dennick (2011). Factor structure was explored using principal component analysis (PCA), followed by CFA to assess structural validity and model fitBoateng, Neilands, Frongillo, Melgar-Quinonez, & Young (2018). Kendall’s W test was used to evaluate expert consensus during Delphi rounds, ensuring consistency in item development and refinementNiederberger & Spranger (2020). These analyses supported the final refinement of the scale and confirmed its alignment with the proposed theoretical model. 3. Results 3.1. Study design and participants This study actually collected 434 valid questionnaires, the general situation is detailed in Table 4. The distribution of "total scores" is shown in Table 5, with a total score ranging from 28 to 80 points. The minimum value (M) is 28 points, the maximum value (X) is 80 points, and the average value (E) is 72.46 points, indicating an overall level slightly above average. For specific statistical results (Table 5). 3.2. Reliability, Validity, and Factorial Structure of the Faculty Competency Scale The internal consistency reliability of the scale was assessed using Cronbach’s alpha, yielding a coefficient of 0.979 across 16 items. This result indicates excellent internal consistency, demonstrating that the instrument possesses high reliability and stability in measuring faculty competencies in medical simulation education. To further explore the underlying structure of the competency variables, PCA was conducted. The Kaiser-Meyer-Olkin (KMO) measure was 0.961, suggesting strong sampling adequacy, while Bartlett’s test of sphericity was significant (χ² = 9881.023, df = 120, p < 0.001), confirming the suitability of the data for factor analysis. The component score covariance matrix indicated zero covariance between the two extracted components, demonstrating that the rotated factors were orthogonal and uncorrelated. The total variance explained by the first two principal components was substantial, with eigenvalues of 12.174 and 1.241, accounting for 76.085% and 7.755% of the variance, respectively, and a cumulative variance of 83.840%. The scree plot illustrated a marked drop in eigenvalues after the second component, supporting the decision to retain two factors. The unrotated component matrix revealed initial loadings of variables on the components, and after rotation (converged in three iterations), the rotated solution provided a clearer factor structure. The first component showed high loadings on traditional teaching competencies such as teamwork, curriculum design and organization, and assessment of teaching effectiveness. The second component demonstrated high loadings on competencies related to GenAI, including the ability to guide students in using GenAI for learning and the ability to analyze data using GenAI-related tools. Communality estimates showed that all variables were well represented by the extracted factors. For example, the communality for the ability to guide students in GenAI-based learning was 0.913, and for teamwork, it was 0.868, indicating that the two principal components effectively captured the underlying variance in these measures. Finally, a Kendall’s coefficient of concordance analysis based on the 16 items revealed a strong consensus among participants (W = 0.761, χ²(433) = 5270.561, p < 0.001), suggesting that medical students exhibited a high degree of agreement in their ranking of faculty competency dimensions. 3.3. Results of confirmatory factor analysis Factor loadings and convergent validity results are presented in Table 8. The latent construct “Traditional Competencies in Medical Simulation Education” was measured by nine observed variables, with standardized factor loadings ranging from 0.842 to 0.935, all statistically significant (p < 0.05). The construct “Innovative Integration of GenAI in Medical Simulation Education” was measured by seven observed variables, with standardized loadings ranging from 0.816 to 0.961, also all significant (p 0.80), indicating strong explanatory power for the corresponding latent constructs. The Average Variance Extracted (AVE) for the two constructs were 0.806 and 0.821, respectively, while their Composite Reliability (CR) values were 0.974 and 0.970. These results satisfy the commonly accepted thresholds of AVE > 0.5 and CR > 0.7, indicating strong internal consistency and good convergent validity. As shown in Table 9, the square roots of AVE values exceeded the off-diagonal Pearson correlation coefficients, confirming satisfactory discriminant validity between the two latent constructs. The results of model fit analysis are presented in Figure 3. The chi-square to degrees of freedom ratio was χ²/df = 6.893, with CFI = 0.939, RMSEA = 0.117, RMR = 0.012, and NFI = 0.929. While some indices approached the ideal thresholds, the overall model demonstrated acceptable goodness-of-fit. The standardized covariance estimate between the two latent constructs was 0.837, with a z-value of 11.323 (p < 0.05), indicating a significant positive correlation. Regarding residuals, all measurement item residual estimates had z-values greater than 10 with p = 0, supporting the appropriateness of residual estimation and further confirming the model’s validity. 4. Discussion 4.1. Novel Contributions This study makes a significant theoretical contribution by addressing the lack of a standardized assessment tool for faculty competence in GenAI-integrated medical simulation education. Grounded in the perspectives of medical students and the TPACK framework, the Med-TPACK Scale was developed and validated, incorporating the practical demands of simulation-based teaching. For the first time, three emerging competency dimensions—Digital LiteracyAydinlar et al. (2024), Contextual FeedbackLane & Roberts (2022), and AI Ethics ScrutinyParanjape, Schinkel, Nannan Panday, Car, & Nanayakkara (2019)—were systematically integrated. Compared to traditional faculty evaluation frameworks, this scale responds to the urgent demands of digital transformation in medical education by incorporating GenAI-adaptive instructional competenciesCervantes et al. (2024);Chan & Hu (2023). Building upon the foundational TPACK modelThyssen, Huwer, Irion, & Schaal (2023), this study introduces a dynamic capability evolution mechanism, which addresses the tension between rapid technological advancement and pedagogical adaptability. This theoretical expansion contributes to a fourth-generation competency evaluation framework for medical simulation educationMeguerdichian, Bajaj, & Walker (2021), bridging a critical measurement gap in technology integration dimensionsMeguerdichian, Bajaj, & Walker (2021). Two key conceptual breakthroughs are achieved: the proposed construct of Technologically Enhanced Content Knowledge aligns with others vision of cultivating medical digital literacy by integrating GenAI tools into simulation data interpretationJanumpally, Nanua, Ngo, & Youens (2024b); and the Ethical-Constraint Technology Knowledge dimension expands the AI ethical decision-making framework, enabling the quantification of ethical competencies in simulation-based teaching for the first time. Methodologically, this study establishes a hybrid, multi-stage development paradigm for medical education assessment tools. The research employs a "DELPHI–EFA–CFA" three-phase validation chain. In the Delphi phase, expert–student dual-perspective consensus building significantly enhanced content validity (S-CVI = 0.92). During the EFA, a cross-loading dynamic threshold rule (Δ ≥ 0.2) was proposedZhuang et al. (2025), providing a replicable statistical decision protocol for similar studies. CFA verified a second-order factor structure (CFI = 0.939, RMSEA = 0.117), confirming the robustness of the theoretical model. This methodology framework effectively balances theory-driven and data-driven tool development. The dual-perspective Delphi design prevents group bias, the dynamic EFA rule strengthens factor classification rigor, and second-order CFA enhances ecological validity in tool construction. Structural Equation Modeling (SEM) further supports the tool’s validity, showing significant associations between traditional simulation competencies (e.g., clinical practice experience, standardized path coefficient = 0.842; effective teaching methods, 0.875) and GenAI-integrated competencies (e.g., GenAI tool utilization, 0.816; GenAI-driven optimization, 0.883), with an overall path coefficient of 0.837. This model quantitatively illustrates the contribution of each competency dimension, offering prioritization guidance for targeted faculty development. 4.2. Practical value The findings offer high practical value in empowering precision faculty development within simulation-based medical education. Only 31.34% of participants reported never using GenAI tools, indicating growing acceptance of this technology in medical training contexts. The average demand score for GenAI-integrated faculty competencies (E = 72.46) reflects a moderately high expectation among students for technology-empowered educatorsSchaye & Triola (2024), aligning with the multidimensional capability demands of Mixed Reality (MR)-based instructional environmentsHersh (2025). The confirmatory factor analysis confirmed the tool’s measurement value, with all standardized factor loadings exceeding 0.8 (p < 0.05), strong convergent validity, and a cumulative explained variance of 83.84%. High communalities for variables such as “guiding students in using GenAI tools” (0.913) and “team collaboration skills” (0.868) demonstrate the synergy between GenAI-related and traditional instructional competenciesMoreira-Choez et al. (2024);Padovano & Cardamone (2024). Based on these findings, the Med-TPACK Scale can support a three-phase dynamic intervention model. In the diagnostic phase, the tool can detect specific competency gaps within MR simulation scenarios, such as insufficient integration of GenAI technologies. During the intervention phase, it enables the construction of personalized development pathways grounded in capability–demand matching. In the monitoring phase, it can longitudinally track the impact of GenAI integration on evolving competency structures, providing evidence-based guidance for medical education policymaking. This closed-loop mechanism of diagnosis, intervention, and monitoring supports a data-driven paradigm shift in faculty training—from experiential and reactive approaches to precision, systemic development. 4.3. Limitation This study presents two key methodological limitations. First, the number of experts involved in the initial round of the Delphi process (n = 5) was slightly below the recommended threshold (n ≥ 8), which may have constrained the achievement of theoretical saturation. Future studies should aim to recruit a more diverse panel of experts, including algorithm engineers and healthcare big data architects, to improve the disciplinary representativeness and enhance the ecological validity of the scale within GenAI-integrated contexts. Additionally, the regional and cultural homogeneity of the participant sample limits the generalizability of the findings. Second, although expert consensus and theoretical validity were achieved (S-CVI/UA = 0.81), the current study has not yet established application-based validity in authentic clinical simulation scenarios. To address this gap, the next phase of research will conduct empirical validation across diverse instructional modalities and integrated technology platforms—such as VR/MR/GenAI hybrid systems—to reinforce the clinical translational validity of the scale and support its implementation in real-world teaching environments. 5. Conclusion Traditional medical simulation and GenAI technologies each offer distinct advantages in medical education. This study provides empirical evidence supporting their synergistic integration, with a significant path coefficient (β = 0.837) indicating strong co-developmental potential. Key competency dimensions such as clinical practice experience (β = 0.842) and GenAI-driven instructional optimization (β = 0.883) demonstrated robust associations, underscoring the transformative impact of combining conventional pedagogical strengths with emerging technological capabilities. To advance this integration, future efforts should prioritize the development of comprehensive technology–pedagogy fusion frameworks. These should be supported by interdisciplinary faculty training, dynamic competency-based interventions, and continuous optimization of the instructional ecosystem. Furthermore, addressing ethical and governance challenges inherent to GenAI applications will be essential. Ultimately, this integrated approach can catalyze a paradigm shift in medical education—from experience-based teaching models to precision-driven, data-informed faculty empowerment. List of abbreviations GenAI = Generative Artificial Intelligence TPACK = Technological Pedagogical Content Knowledge EFA = Exploratory Factor Analysis CFA = Confirmatory Factor Analysis CK = content knowledge PK = pedagogical knowledge PCA = principal component analysis KMO = Kaiser-Meyer-Olkin AVE = Average Variance Extracted CR = Composite Reliability CFI = Comparative Fit Index RMSEA = Root Mean Square Error of Approximation NFI = Normed Fit Index SEM = Structural Equation Modeling S-CVI/UA= Scale-level Content Validity Index based on Universal Agreement Declarations 7.1. Ethics approval and consent to participate This study was approved by the Institutional Ethics Committee of Wenzhou Medical University Second Affiliated Hospital, with Ethical Approval Number: XY-2024-003. Data collection was conducted between February 14 and March 28, 2025. All participants were informed about the purpose, procedures, and confidentiality measures of the study, and provided written informed consent prior to participation. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. 7.2. Consent for publication All participants provided informed consent for the publication of anonymized data and research findings. 7.3. Availability of data and materials We used anonymize data which are currently stored in our local data warehouse. The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. 7.4. Competing interests The authors declare that they have no competing interests. 7.5. Funding This study was supported by the Special Fund for Clinical Scientific Research of Wu Jieping Medical Foundation (Grant No. 3206750, 2020-10-36). 7.6. Authors’ contributions Y. Zhuge conceived the study design, led the development of the Med-TPACK Scale, and was the major contributor in writing the manuscript. H. Mei contributed to the Delphi expert coordination and supported the data collection and analysis. X. Yao assisted in the construction of the statistical validation framework and contributed to the interpretation of CFA and SEM results. H. Yao provided methodological guidance. Q. Chen contributed to the writing and revision of the discussion section. All authors contributed to the manuscript revision, read, and approved the final version. 7.7. Acknowledgments We would like to thank the Wu Jieping Medical Foundation for their financial support through the Special Fund of Clinical Scientific Research (No. 3206750, 2020-10-36), which enabled the completion of this study. 7.8. Clinical trial number This study was not registered as a clinical trail. 7.9. 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Computers and Education: Artificial Intelligence , 7, 100256. doi: https://doi.org/10.1016/j.caeai.2024.100256 Paranjape, K., Schinkel, M., Nannan Panday, R., Car, J., & Nanayakkara, P. (2019). Introducing Artificial Intelligence Training in Medical Education. [Journal Article]. JMIR Med Educ , 5(2), e16048. doi: 10.2196/16048 Polit, D. F., & Beck, C. T. (2006). The content validity index: are you sure you know what's being reported? Critique and recommendations. [Journal Article]. Res Nurs Health , 29(5), 489-497. doi: 10.1002/nur.20147 Schaye, V., & Triola, M. M. (2024). The generative artificial intelligence revolution: How hospitalists can lead the transformation of medical education. [Journal Article]. J Hosp Med , 19(12), 1181-1184. doi: 10.1002/jhm.13360 Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. [Editorial]. Int J Med Educ , 2, 53-55. doi: 10.5116/ijme.4dfb.8dfd Thyssen, C., Huwer, J., Irion, T., & Schaal, S. (2023). From TPACK to DPACK: The “Digitality-Related Pedagogical and Content Knowledge”-Model in STEM-Education Education Sciences (13, pp.). (Reprinted. Weidener, L., & Fischer, M. (2024). Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education. [Journal Article]. JMIR Med Educ , 10, e55368. doi: 10.2196/55368 Zhuang, H., Zhao, H., Wang, Y., He, C., Zhai, J.,... Wang, B. (2025). Development and validation of a school satisfaction scale for medical students. [Journal Article; Validation Study]. BMC Med Educ , 25(1), 379. doi: 10.1186/s12909-025-06962-w Tables Table 1. Characteristics of Pre-test Sample (n=30) ‌ ‌Item‌ ‌Content‌ ‌Frequency‌ ‌Percentage (%)‌ ‌Cumulative Percentage (%)‌ ‌ Gender ‌ Male 20 66.67 66.67 Female 10 33.33 100.00 ‌ Age Group ‌ 18-25 years 28 93.33 93.33 31-40 years 2 6.67 100.00 ‌ Education Level ‌ Associate Degree (In Progress) 1 3.33 3.33 Bachelor's Degree 10 33.33 36.67 Master's Degree 19 63.33 100.00 ‌ Major Category ‌ Clinical Medicine 25 83.30 83.30 Medical Technology 3 10.00 93.30 Nursing 2 6.67 100.00 ‌ GenAI Application Experience ‌ Never used 9 30.00 30.00 Used 1-2 types 15 50.00 80.00 Used 3-5 types 6 20.00 100.00 Table 2. Demand Scale for Educator Competency in GenAI-Integrated Medical Simulation Education (16-80 points) ‌Category‌ ‌Item‌ ‌1‌ ‌2‌ ‌3‌ ‌4‌ ‌5‌ ‌ Traditional Medical Simulation Teaching & Literacy Competency ‌ ‌ 1. Solid Medical Expertise ‌: Mastery of foundational and clinical medical knowledge; ability to accurately explain diseases. ‌ 2. Rich Clinical Experience ‌: Extensive clinical practice and case integration for teaching. ‌ 3. Effective Teaching Methodology ‌: Proficiency in diverse teaching methods and adaptive application. ‌ 4. Strong Communication Skills ‌: Clear and precise communication with students and patients, providing actionable guidance. ‌ 5. Learning Facilitation ‌: Guiding students to develop learning plans and autonomous study habits. ‌ 6. Curriculum Design & Organization ‌: Logical structuring of content and progress. ‌ 7. Teaching Evaluation ‌: Objective assessment of student outcomes and feedback delivery. ‌ 8. Team Collaboration ‌: Effective coordination with educators and healthcare professionals to foster student development. ‌ 9. Professional Ethics & Humanistic Literacy ‌: Adherence to medical ethics, patient-centered care, and exemplary conduct. GenAI-Technology Integration & Innovation Competency ‌ ‌ 10. GenAI Tool Proficiency ‌: Skilled use of generative AI tools (e.g., ChatGPT, DeepSeek, DALL-E, medical diagnostic AI) for lesson preparation and case generation. ‌ 11. GenAI Content Verification ‌: Ability to evaluate accuracy and reliability of AI-generated medical information. ‌ 12. GenAI-Driven Curriculum Design ‌: Leveraging AI to analyze student data and tailor personalized teaching plans. ‌ 13. GenAI Ethics & Safety ‌: Knowledge of ethical guidelines (e.g., data privacy, AI diagnostic accountability) for medical AI applications. ‌ 14. GenAI-Guided Learning ‌: Training students to use GenAI tools for knowledge acquisition and case analysis with critical thinking. ‌ 15. GenAI Data Analysis ‌: Interpreting AI-generated teaching data (e.g., student knowledge/skill metrics) to evaluate outcomes. ‌ 16. GenAI-Teaching Integration Innovation ‌: Pioneering novel methods (e.g., AI-enhanced simulation cases) to diversify pedagogy. Table 3. Input Parameters Parameter Value Description Test Family Correlation and regression Used to analyze the correlation between scale items and total scores to determine sample size Statistical Test Correlation: Point - biserial correlation Analyzes the correlation between items (similar to binary variable characteristics) and scale total scores (continuous variables) Effect Size (r) 0.3 Anticipated moderate correlation between items and scale total scores Statistical Power (1−β) 0.8 80% confidence in detecting true associations between items and total scores Significance Level (α) 0.05 Two-tailed test, setting the probability of committing a Type I error at 5% Number of Variables (Items) 16 Number of items in the teaching ability scale ‌ Table 4. Analysis Results Indicator Value Standard Reference Value Statistical Power (1−β) 0.81 (81%) Ideal value ≥ 0.8 Actual Effect Size (r) 0.3 (preset) — Critical r Value ±0.16 (df=418) Reference value for judging significant correlation Required Minimum Sample Size 418 individuals — Table 5. Stratified Sampling Results Professional Category Valid Questionnaires Proportion of Total Sample Intern Proportion (Within Stratum) Proportion of Individuals Within One Year of Graduation (Within Stratum) Clinical 312 62.00% 50.3% (157/312) 49.7% (155/312) Nursing 64 15.00% 53.1% (34/64) 46.9% (30/64) Medical Technology 58 13.00% 51.7% (30/58) 48.3% (28/58) Table 6: General Information (n=434) Item Content Number (n) Percentage (%) ‌Gender‌ Male 189 43.55 Female 245 56.45 ‌Age Group‌ 18-25 years 356 82.03 26-30 years 50 11.52 31-40 years 23 5.30 41-50 years 5 1.15 ‌Education Level‌ Associate Degree (In Progress) 6 1.38 Bachelor's Degree (In Progress) 196 45.16 Associate Degree 32 7.37 Bachelor's Degree 122 28.11 Master's Degree 78 17.97 ‌Major‌ Nursing 58 13.36 Medical Technology 64 14.75 Clinical Medicine 312 71.89 ‌GenAI Application Experience‌ Never used 136 31.34 Used 1-2 types 220 50.69 Used 3-5 types 69 15.90 Used 6 or more types 9 2.07 ‌ Table 7. Distribution of Total Scores (n=434) ‌ Number Minimum (M) Maximum (X) Average (E) Standard Deviation 434 28 80 72.46 9.402 Table 8. Reliability and Convergent Validity Results atent Variable AVE CR Number of Manifest Variables Cronbach's Alpha Traditional Medical Simulation Teaching & Competency 0.806 0.974 9 - GenAI Technology & Medical Simulation Teaching Integration Innovation Competency 0.821 0.97 7 - Table 9. Discriminant Validity Results Latent Variable 1 Square Root of AVE Latent Variable 2 Pearson Correlation Coefficient Traditional Medical Simulation Teaching & Competency 0.898 GenAI Technology & Medical Simulation Teaching Integration Innovation Competency 0.815 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviews received at journal 08 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 22 May, 2025 Submission checks completed at journal 22 May, 2025 First submitted to journal 22 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6595296","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466615511,"identity":"c2698306-5c74-45a2-8f1b-c90b4d7006fc","order_by":0,"name":"Ying Zhuge","email":"","orcid":"","institution":"Second Affiliated Hospital \u0026 Yuying Children's Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhuge","suffix":""},{"id":466615512,"identity":"edae6611-ee6a-4c67-b795-e13aacd1b3d6","order_by":1,"name":"Xintong Yao","email":"","orcid":"","institution":"Wenzhou 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15:31:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1953958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6595296/v1/c39977c9-0ac8-4c99-bb4a-b0d1c7b88bf5.pdf"},{"id":84326680,"identity":"1cd7b439-424f-46bb-a1f7-111560e57a2f","added_by":"auto","created_at":"2025-06-10 15:15:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20918,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6595296/v1/eddd28877485032a450558e6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of a faculty competency model for medical simulation education integrated with GenAI: A mixed study based on the perspective of medical students","fulltext":[{"header":"1.\tBackground","content":"\u003cp\u003eGenerative Artificial Intelligence (GenAI) is fundamentally reshaping the landscape of medical simulation education by dynamically integrating clinical guidelines, evidence-based resources, and multimodal case data into a continuously evolving knowledge network\u0026nbsp;Bland (2025);Janumpally, Nanua, Ngo, \u0026amp; Youens (2024a). This transformation is not merely technical\u0026mdash;it redefines pedagogical roles, fostering a triadic model of collaborative decision-making among students, AI, and educatorsCheung et al. (2024). As a result, simulation educators must transition from traditional \u0026quot;knowledge authorities\u0026quot; to \u0026quot;knowledge navigators,\u0026quot; requiring advanced competencies in critically screening, validating, and adapting AI-generated content throughout the technology adoption curve (honeymoon, frustration, adaptation, and acceptance phasesCheung et al. (2024).\u003c/p\u003e\n\u003cp\u003eHowever, existing studies primarily explore GenAI through fragmented lenses\u0026mdash;such as isolated applications (e.g., virtual case generation) or basic teacher training workshops(like AI tool workshops)\u0026mdash;without addressing the need for systemic integration of GenAI into the broader framework of simulation-based medical educationGrunhut, Wyatt, \u0026amp; Marques (2021). Two major gaps persist: first, a disproportionate emphasis on technical functionalities at the expense of holistic pedagogical transformationGrunhut, Wyatt, \u0026amp; Marques (2021); second, the absence of dynamic, learner-centered analyses that reflect the evolving cognitive needs of students and the resulting generational shift in educational expectationsGrunhut, Marques, \u0026amp; Wyatt (2022). This misalignment hampers the effectiveness of current faculty development strategies in supporting sustainable, AI-driven transformation.\u003c/p\u003e\n\u003cp\u003eTo address these challenges, this study proposes the construction of a standardized faculty competency model tailored to GenAI-integrated medical simulation education. By deconstructing the synergistic relationship between traditional teaching competencies and GenAI-specific capabilitiesGrunhut, Marques, \u0026amp; Wyatt (2022), it aims to provide a validated empirical foundation to guide faculty development and institutional reform in the intelligent era.\u003c/p\u003e"},{"header":"2.\tMethods","content":"\u003ch2\u003e\u003cem\u003e2.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e \u003cem\u003eAim\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study aims to construct and validate a competency model integrating GenAI with traditional instructional competencies for medical simulation educators.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Study design\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eA mixed-methods approach was employed, incorporating the Delphi techniqueHong et al. (2019)\u0026nbsp;and a cross-sectional survey for empirical validation. Details of the survey questionnaire are provided in Supplemental material.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e Scale development and validation\u003c/h2\u003e\n\u003ch3\u003e\u003cem\u003e2.3.1.\u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eTechnological Pedagogical Content Knowledge (TPACK)\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe competency assessment scale was developed based on the Technological Pedagogical Content Knowledge (TPACK) framework, which emphasizes the dynamic interaction among content knowledge (CK), pedagogical knowledge (PK), and technological knowledge, serving as a foundational model for faculty competency development in the information ageAit Ali et al. (2023).\u003c/p\u003e\n\u003cp\u003eThe scale included two primary dimensions: (1) Traditional Teaching Competence (9 items), grounded in the integration of CK and PK, focused on core pedagogical abilities in medical simulation education. Items such as \"Clinical Case Teaching Design Ability\" reflected Shulman’s\u003csup\u003e6\u003c/sup\u003e concept of pedagogical content knowledge, emphasizing the alignment of teaching strategies with disciplinary content. (2) GenAI Integration Ability (7 items), designed in accordance with TPACK’s principle of contextual technology adaptation, assessed educators’ capacity to align GenAI tools with specific simulation scenarios. Items such as \"Dynamic Matching of Technical Tools and Simulation Scenarios\" and \"Information Reliability Assessment\" addressed ethical considerations and instructional alignment, ensuring that technology use enhances rather than overshadows pedagogy, and avoiding the risk of tool alienation.\u003c/p\u003e\n\u003cp\u003eThe item development adhered to TPACK’s context-sensitive and ethically aware principles, incorporating elements such as cross-disciplinary collaboration and information credibility to address challenges specific to GenAI integration in medical training.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e2.3.2.\u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u003cem\u003eDelphi Method\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;design and verification\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo ensure theoretical rigor and practical relevance,experts were required to meet both of the following criteria: (1) at least 10 years of clinical teaching experience, and (2) demonstrated familiarity with the TPACK framework.\u003c/p\u003e\n\u003cp\u003eIn the first round of the Delphi process, five experts participated, including three clinical medicine specialists and two medical education scholars. All held senior professional titles and had extensive experience in simulation-based teaching and faculty development. To ensure balanced representation of the “technology–pedagogy–content” triad emphasized in the TPACK model, the content validity standard proposed by Beck (S-CVI/Ave ≥ 0.90) was employedPolit \u0026amp; Beck (2006). This rigorous threshold helped prevent undue emphasis on technical functionality during item formulation and promoted alignment with educational theory.\u003c/p\u003e\n\u003cp\u003eFollowing distribution of the initial questionnaire, expert agreement was evaluated using Kendall’s coefficient of concordance, revealing a high level of consensus (W = 0.83, χ² = 53.12, df = 4, p \u0026lt; 0.001). Based on qualitative feedback, several items were revised for improved clarity and operational precision—for example, clearer definitions were provided for “teamwork competence” and “GenAI information discernment ability.” In the second round of consultation, the revised scale demonstrated even stronger expert consensus (W = 0.84, χ² = 53.50, df = 4, p \u0026lt; 0.001), indicating increasing convergence toward a theoretically coherent and practically relevant instrument.\u003c/p\u003e\n\u003cp\u003eTo further verify content validity, a validation panel of 10 participants was assembled, including five experts in medical simulation education (three clinical educators and two medical education researchers, all with senior professional titles and over 10 years of experience) and five learners (three senior undergraduate interns and two resident physicians), each with exposure to more than 20 simulation-based training sessions. Using a four-point Likert scale (1 = not relevant, 4 = highly relevant), item-level content validity indices (I-CVI) ranged from 0.70 to 1.00. At the scale level, both the universal agreement index (S-CVI/UA = 0.81) and the average agreement index (S-CVI/Ave = 0.96) met the quality benchmarks recommended by Polit and BeckPolit \u0026amp; Beck (2006), confirming the strong content validity of the scale, indicating that the revised items more accurately reflected both technological integration and pedagogical expectations in medical simulation contexts.\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e2.3.3.\u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u003cem\u003epreliminary experiment design and verification\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eParticipants were randomly selected from individuals who had completed at least ten sessions of medical simulation-based training. To ensure immediacy and contextual relevance, questionnaires were distributed within 30 minutes after the course (Supplemental material). All 30 responses were valid, yielding a 100% effective response rate. Sample characteristics are detailed in Table 1.\u003c/p\u003e\n\u003cp\u003eThe assessment instrument employed a five-point Likert scale (1 = very unimportant, 5 = very important), with total scores ranging from 16 to 80. Based on principal component analysis, two latent factors were extracted, jointly explaining 69.086% of the total variance (Figure 1). The first factor, representing traditional medical simulation teaching competency, accounted for 38.674% of the variance and featured high loadings on items such as \"Communication Skills\" (loading = 0.862) and \"Course Design and Organization Skills\" (loading = 0.859). The second factor, reflecting the integration and innovation of GenAI technology in medical simulation education, accounted for 30.412% of the variance, with strong item loadings such as \"GenAI Ethics and Safety Knowledge\" (loading = 0.863) and \"Ability to Guide Students in Using GenAI for Learning\" (loading = 0.818).\u003c/p\u003e\n\u003cp\u003eContent validity was established through reference to theoretical framework for integrated medical educationPolit \u0026amp; Beck (2006), ensuring comprehensive coverage of both pedagogical and technological domains. The overall internal consistency of the scale was excellent, with a Cronbach’s α coefficient of 0.932. Reliability for each dimension was also high, with α = 0.948 for the traditional teaching competency domain and α = 0.900 for the GenAI integration domain. The \"GenAI integrated medical simulation education faculty competency needs table\" wad shown in Table 2.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Sampling and evaluation\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eSample size estimation using G*Power 3.1 software determined that a minimum of 418 participants was required for adequate statistical power (Table 3 and Table 4). Participants were drawn from professional healthcare personnel who had attended at least ten medical simulation sessions between February 14 and March 28, 2025, at a tertiary hospital’s Clinical Skills Training Center (Ethical Approval No. XY-2024-003). All data were de-identified prior to analysis.\u003c/p\u003e\n\u003cp\u003eProportional stratified sampling was implemented based on historical training participation rates across three professional categories: clinical medicine, nursing, and medical technology (approximate ratio 6:1.5:1.3). Attendance records (N=1286) served as the sampling frame, including job type (e.g., interns or professionals within one year post-graduation). Within each stratum, subjects were assigned unique identification codes. Random sampling was conducted using Excel’s RANDBETWEEN function, with duplicates removed and additional draws performed as needed to meet target quotas (Table 5).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Data analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eDescriptive statistics were used to summarize demographic characteristics and item responses. Internal consistency was evaluated using Cronbach’s α coefficientTavakol \u0026amp; Dennick (2011). Factor structure was explored using principal component analysis (PCA), followed by CFA to assess structural validity and model fitBoateng, Neilands, Frongillo, Melgar-Quinonez, \u0026amp; Young (2018). Kendall’s W test was used to evaluate expert consensus during Delphi rounds, ensuring consistency in item development and refinementNiederberger \u0026amp; Spranger (2020). These analyses supported the final refinement of the scale and confirmed its alignment with the proposed theoretical model.\u003c/p\u003e"},{"header":"3.\tResults ","content":"\u003ch2\u003e\u003cem\u003e3.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Study design and participants\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study actually collected 434 valid questionnaires, the general situation is detailed in Table 4. The distribution of \"total scores\" is shown in Table 5, with a total score ranging from 28 to 80 points. The minimum value (M) is 28 points, the maximum value (X) is 80 points, and the average value (E) is 72.46 points, indicating an overall level slightly above average. For specific statistical results (Table 5).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Reliability, Validity, and Factorial Structure of the Faculty Competency Scale\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe internal consistency reliability of the scale was assessed using Cronbach’s alpha, yielding a coefficient of 0.979 across 16 items. This result indicates excellent internal consistency, demonstrating that the instrument possesses high reliability and stability in measuring faculty competencies in medical simulation education. To further explore the underlying structure of the competency variables, PCA was conducted. The Kaiser-Meyer-Olkin (KMO) measure was 0.961, suggesting strong sampling adequacy, while Bartlett’s test of sphericity was significant (χ² = 9881.023, df = 120, p \u0026lt; 0.001), confirming the suitability of the data for factor analysis. The component score covariance matrix indicated zero covariance between the two extracted components, demonstrating that the rotated factors were orthogonal and uncorrelated.\u003c/p\u003e\n\u003cp\u003eThe total variance explained by the first two principal components was substantial, with eigenvalues of 12.174 and 1.241, accounting for 76.085% and 7.755% of the variance, respectively, and a cumulative variance of 83.840%. The scree plot illustrated a marked drop in eigenvalues after the second component, supporting the decision to retain two factors. The unrotated component matrix revealed initial loadings of variables on the components, and after rotation (converged in three iterations), the rotated solution provided a clearer factor structure. The first component showed high loadings on traditional teaching competencies such as teamwork, curriculum design and organization, and assessment of teaching effectiveness. The second component demonstrated high loadings on competencies related to GenAI, including the ability to guide students in using GenAI for learning and the ability to analyze data using GenAI-related tools.\u003c/p\u003e\n\u003cp\u003eCommunality estimates showed that all variables were well represented by the extracted factors. For example, the communality for the ability to guide students in GenAI-based learning was 0.913, and for teamwork, it was 0.868, indicating that the two principal components effectively captured the underlying variance in these measures. Finally, a Kendall’s coefficient of concordance analysis based on the 16 items revealed a strong consensus among participants (W = 0.761, χ²(433) = 5270.561, p \u0026lt; 0.001), suggesting that medical students exhibited a high degree of agreement in their ranking of faculty competency dimensions.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Results of confirmatory factor analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eFactor loadings and convergent validity results are presented in Table 8. The latent construct “Traditional Competencies in Medical Simulation Education” was measured by nine observed variables, with standardized factor loadings ranging from 0.842 to 0.935, all statistically significant (p \u0026lt; 0.05). The construct “Innovative Integration of GenAI in Medical Simulation Education” was measured by seven observed variables, with standardized loadings ranging from 0.816 to 0.961, also all significant (p \u0026lt; 0.05). All items demonstrated high factor loadings (\u0026gt;0.80), indicating strong explanatory power for the corresponding latent constructs. The Average Variance Extracted (AVE) for the two constructs were 0.806 and 0.821, respectively, while their Composite Reliability (CR) values were 0.974 and 0.970. These results satisfy the commonly accepted thresholds of AVE \u0026gt; 0.5 and CR \u0026gt; 0.7, indicating strong internal consistency and good convergent validity. As shown in Table 9, the square roots of AVE values exceeded the off-diagonal Pearson correlation coefficients, confirming satisfactory discriminant validity between the two latent constructs.\u003c/p\u003e\n\u003cp\u003eThe results of model fit analysis are presented in Figure 3. The chi-square to degrees of freedom ratio was χ²/df = 6.893, with CFI = 0.939, RMSEA = 0.117, RMR = 0.012, and NFI = 0.929. While some indices approached the ideal thresholds, the overall model demonstrated acceptable goodness-of-fit. The standardized covariance estimate between the two latent constructs was 0.837, with a z-value of 11.323 (p \u0026lt; 0.05), indicating a significant positive correlation. Regarding residuals, all measurement item residual estimates had z-values greater than 10 with p = 0, supporting the appropriateness of residual estimation and further confirming the model’s validity.\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003ch2\u003e\u003cem\u003e4.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u003cem\u003eNovel Contributions\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study makes a significant theoretical contribution by addressing the lack of a standardized assessment tool for faculty competence in GenAI-integrated medical simulation education. Grounded in the perspectives of medical students and the TPACK framework, the Med-TPACK Scale was developed and validated, incorporating the practical demands of simulation-based teaching. For the first time, three emerging competency dimensions—Digital LiteracyAydinlar et al. (2024), Contextual FeedbackLane \u0026amp; Roberts (2022), and AI Ethics ScrutinyParanjape, Schinkel, Nannan Panday, Car, \u0026amp; Nanayakkara (2019)—were systematically integrated. Compared to traditional faculty evaluation frameworks, this scale responds to the urgent demands of digital transformation in medical education by incorporating GenAI-adaptive instructional competenciesCervantes et al. (2024);Chan \u0026amp; Hu (2023). Building upon the foundational TPACK modelThyssen, Huwer, Irion, \u0026amp; Schaal (2023), this study introduces a dynamic capability evolution mechanism, which addresses the tension between rapid technological advancement and pedagogical adaptability. This theoretical expansion contributes to a fourth-generation competency evaluation framework for medical simulation educationMeguerdichian, Bajaj, \u0026amp; Walker (2021), bridging a critical measurement gap in technology integration dimensionsMeguerdichian, Bajaj, \u0026amp; Walker (2021). Two key conceptual breakthroughs are achieved: the proposed construct of Technologically Enhanced Content Knowledge aligns with others vision of cultivating medical digital literacy by integrating GenAI tools into simulation data interpretationJanumpally, Nanua, Ngo, \u0026amp; Youens (2024b); and the Ethical-Constraint Technology Knowledge dimension expands the AI ethical decision-making framework, enabling the quantification of ethical competencies in simulation-based teaching for the first time.\u003c/p\u003e\n\u003cp\u003eMethodologically, this study establishes a hybrid, multi-stage development paradigm for medical education assessment tools. The research employs a \"DELPHI–EFA–CFA\" three-phase validation chain. In the Delphi phase, expert–student dual-perspective consensus building significantly enhanced content validity (S-CVI = 0.92). During the EFA, a cross-loading dynamic threshold rule (Δ ≥ 0.2) was proposedZhuang et al. (2025), providing a replicable statistical decision protocol for similar studies. CFA verified a second-order factor structure (CFI = 0.939, RMSEA = 0.117), confirming the robustness of the theoretical model. This methodology framework effectively balances theory-driven and data-driven tool development. The dual-perspective Delphi design prevents group bias, the dynamic EFA rule strengthens factor classification rigor, and second-order CFA enhances ecological validity in tool construction. Structural Equation Modeling (SEM) further supports the tool’s validity, showing significant associations between traditional simulation competencies (e.g., clinical practice experience, standardized path coefficient = 0.842; effective teaching methods, 0.875) and GenAI-integrated competencies (e.g., GenAI tool utilization, 0.816; GenAI-driven optimization, 0.883), with an overall path coefficient of 0.837. This model quantitatively illustrates the contribution of each competency dimension, offering prioritization guidance for targeted faculty development.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Practical value\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe findings offer high practical value in empowering precision faculty development within simulation-based medical education. Only 31.34% of participants reported never using GenAI tools, indicating growing acceptance of this technology in medical training contexts. The average demand score for GenAI-integrated faculty competencies (E = 72.46) reflects a moderately high expectation among students for technology-empowered educatorsSchaye \u0026amp; Triola (2024), aligning with the multidimensional capability demands of Mixed Reality (MR)-based instructional environmentsHersh (2025). The confirmatory factor analysis confirmed the tool’s measurement value, with all standardized factor loadings exceeding 0.8 (p \u0026lt; 0.05), strong convergent validity, and a cumulative explained variance of 83.84%. High communalities for variables such as “guiding students in using GenAI tools” (0.913) and “team collaboration skills” (0.868) demonstrate the synergy between GenAI-related and traditional instructional competenciesMoreira-Choez et al. (2024);Padovano \u0026amp; Cardamone (2024).\u003c/p\u003e\n\u003cp\u003eBased on these findings, the Med-TPACK Scale can support a three-phase dynamic intervention model. In the diagnostic phase, the tool can detect specific competency gaps within MR simulation scenarios, such as insufficient integration of GenAI technologies. During the intervention phase, it enables the construction of personalized development pathways grounded in capability–demand matching. In the monitoring phase, it can longitudinally track the impact of GenAI integration on evolving competency structures, providing evidence-based guidance for medical education policymaking. This closed-loop mechanism of diagnosis, intervention, and monitoring supports a data-driven paradigm shift in faculty training—from experiential and reactive approaches to precision, systemic development.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Limitation\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study presents two key methodological limitations. First, the number of experts involved in the initial round of the Delphi process (n = 5) was slightly below the recommended threshold (n ≥ 8), which may have constrained the achievement of theoretical saturation. Future studies should aim to recruit a more diverse panel of experts, including algorithm engineers and healthcare big data architects, to improve the disciplinary representativeness and enhance the ecological validity of the scale within GenAI-integrated contexts. Additionally, the regional and cultural homogeneity of the participant sample limits the generalizability of the findings. Second, although expert consensus and theoretical validity were achieved (S-CVI/UA = 0.81), the current study has not yet established application-based validity in authentic clinical simulation scenarios. To address this gap, the next phase of research will conduct empirical validation across diverse instructional modalities and integrated technology platforms—such as VR/MR/GenAI hybrid systems—to reinforce the clinical translational validity of the scale and support its implementation in real-world teaching environments.\u003c/p\u003e"},{"header":"5.\tConclusion","content":"\u003cp\u003eTraditional medical simulation and GenAI technologies each offer distinct advantages in medical education. This study provides empirical evidence supporting their synergistic integration, with a significant path coefficient (\u0026beta; = 0.837) indicating strong co-developmental potential. Key competency dimensions such as clinical practice experience (\u0026beta; = 0.842) and GenAI-driven instructional optimization (\u0026beta; = 0.883) demonstrated robust associations, underscoring the transformative impact of combining conventional pedagogical strengths with emerging technological capabilities. To advance this integration, future efforts should prioritize the development of comprehensive technology\u0026ndash;pedagogy fusion frameworks. These should be supported by interdisciplinary faculty training, dynamic competency-based interventions, and continuous optimization of the instructional ecosystem. Furthermore, addressing ethical and governance challenges inherent to GenAI applications will be essential. Ultimately, this integrated approach can catalyze a paradigm shift in medical education\u0026mdash;from experience-based teaching models to precision-driven, data-informed faculty empowerment.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eGenAI = Generative Artificial Intelligence\u003c/p\u003e\n\u003cp\u003eTPACK = Technological Pedagogical Content Knowledge\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEFA = Exploratory Factor Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCFA = Confirmatory Factor Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCK = content knowledge\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePK = pedagogical knowledge\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA = \u0026nbsp;principal component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKMO = Kaiser-Meyer-Olkin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAVE = Average Variance Extracted\u003c/p\u003e\n\u003cp\u003eCR = Composite Reliability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCFI\u003c/strong\u003e = \u003cstrong\u003eComparative Fit Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRMSEA =\u003c/strong\u003e \u003cstrong\u003eRoot Mean Square Error of Approximation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNFI\u003c/strong\u003e = \u003cstrong\u003eNormed Fit Index\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSEM = Structural Equation Modeling\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS-CVI/UA= Scale-level Content Validity Index based on Universal Agreement\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cem\u003e7.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Ethics approval and consent to participate \u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee of Wenzhou Medical University Second Affiliated Hospital, with Ethical Approval Number: XY-2024-003. Data collection was conducted between February 14 and March 28, 2025. All participants were informed about the purpose, procedures, and confidentiality measures of the study, and provided written informed consent prior to participation. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e7.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Consent for publication\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eAll participants provided informed consent for the publication of anonymized data and research findings.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e7.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Availability of data and materials\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eWe used anonymize data which are currently stored in our local data \u0026nbsp;warehouse. The datasets used and analyzed during the current study are \u0026nbsp;available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e7.4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Competing interests\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e7.5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Funding\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Special Fund for Clinical Scientific Research of Wu Jieping Medical Foundation (Grant No. 3206750, 2020-10-36).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e7.6.\u0026nbsp; \u0026nbsp;\u0026nbsp;Authors’ contributions\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eY. Zhuge conceived the study design, led the development of the Med-TPACK Scale, and was the major contributor in writing the manuscript. H. Mei contributed to the Delphi expert coordination and supported the data collection and analysis. X. Yao assisted in the construction of the statistical validation framework and contributed to the interpretation of CFA and SEM results. \u0026nbsp;H. Yao provided methodological guidance. Q. Chen contributed to the writing and revision of the discussion section. All authors contributed to the manuscript revision, read, and approved the final version.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e7.7.\u0026nbsp; \u0026nbsp;\u0026nbsp;Acknowledgments\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eWe would like to thank the Wu Jieping Medical Foundation for their financial support through the Special Fund of Clinical Scientific Research (No. 3206750, 2020-10-36), which enabled the completion of this study.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e7.8.\u0026nbsp; \u0026nbsp;\u0026nbsp;Clinical trial number\u003c/em\u003e\u003c/h2\u003e\n\u003ch2\u003eThis study was not registered as a clinical trail.\u003c/h2\u003e\n\u003ch2\u003e\u003cem\u003e7.9.\u0026nbsp; \u0026nbsp;\u0026nbsp;Author details\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eY. zhuge:\u0026nbsp;Center for Medical Simulation and Education, The Second Affiliated Hospital of Wenzhou Medical University,\u0026nbsp;Wenzhou, Zhejiang, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eX. Yao: Wenzhou Medical University，Wenzhou, Zhejiang, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH. Mei: The Teaching Service, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH. Yao (Corresponding author): Department of Interventional Radiology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQ. Chen: Center for Medical Simulation and Education, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAit Ali, D., El Meniari, A., El Filali, S., Morabite, O., Senhaji, F.,... Khabbache, H. (2023). Empirical Research on Technological Pedagogical Content Knowledge (TPACK) Framework in Health Professions Education: A Literature Review. 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[Journal Article; Review]. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e, 11, 1525604. doi: 10.3389/fmed.2024.1525604\u003c/li\u003e\n\u003cli\u003eLane, A. S., \u0026amp; Roberts, C. (2022). Contextualised reflective competence: a new learning model promoting reflective practice for clinical training. [Journal Article]. \u003cem\u003eBMC Med Educ\u003c/em\u003e, 22(1), 71. doi: 10.1186/s12909-022-03112-4\u003c/li\u003e\n\u003cli\u003eLee, J., Wu, A. S., Li, D., \u0026amp; Kulasegaram, K. M. (2021). Artificial Intelligence in Undergraduate Medical Education: A Scoping Review. [Journal Article; Scoping Review]. \u003cem\u003eAcad Med\u003c/em\u003e, 96(11S), S62-S70. doi: 10.1097/ACM.0000000000004291\u003c/li\u003e\n\u003cli\u003eMartinez-Strengel, A., Balasuriya, L., Black, A., Berg, D., Genao, I., Gross, C. P.,... Boatright, D. (2021). Perspectives of Internal Medicine Residency Program Directors on the Accreditation Council for Graduate Medical Education (ACGME) Diversity Standards. [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov\u0026apos;t]. \u003cem\u003eJ Gen Intern Med\u003c/em\u003e, 36(9), 2539-2546. doi: 10.1007/s11606-021-06825-2\u003c/li\u003e\n\u003cli\u003eMeguerdichian, M. J., Bajaj, K., \u0026amp; Walker, K. (2021). Fundamental underpinnings of simulation education: describing a four-component instructional design approach to healthcare simulation fellowships. [Journal Article]. \u003cem\u003eAdv Simul (Lond)\u003c/em\u003e, 6(1), 18. doi: 10.1186/s41077-021-00171-3\u003c/li\u003e\n\u003cli\u003eMoreira-Choez, J. S., G\u0026oacute;mez Barzola, K. E., Lamus De Rodr\u0026iacute;guez, T. M., Sabando-Garc\u0026iacute;a, A. R., Cruz Mendoza, J. C.,... Cede\u0026ntilde;o Barcia, L. A. (2024). Assessment of digital competencies in higher education faculty: a multimodal approach within the framework of artificial intelligence. \u003cem\u003eFrontiers in Education\u003c/em\u003e, Volume 9 - 2024\u003c/li\u003e\n\u003cli\u003eNiederberger, M., \u0026amp; Spranger, J. (2020). Delphi Technique in Health Sciences: A Map. [Journal Article]. \u003cem\u003eFront Public Health\u003c/em\u003e, 8, 457. doi: 10.3389/fpubh.2020.00457\u003c/li\u003e\n\u003cli\u003ePadovano, A., \u0026amp; Cardamone, M. (2024). Towards human-AI collaboration in the competency-based curriculum development process: The case of industrial engineering and management education. \u003cem\u003eComputers and Education: Artificial Intelligence\u003c/em\u003e, 7, 100256. doi: https://doi.org/10.1016/j.caeai.2024.100256\u003c/li\u003e\n\u003cli\u003eParanjape, K., Schinkel, M., Nannan Panday, R., Car, J., \u0026amp; Nanayakkara, P. (2019). Introducing Artificial Intelligence Training in Medical Education. [Journal Article]. \u003cem\u003eJMIR Med Educ\u003c/em\u003e, 5(2), e16048. doi: 10.2196/16048\u003c/li\u003e\n\u003cli\u003ePolit, D. F., \u0026amp; Beck, C. T. (2006). The content validity index: are you sure you know what\u0026apos;s being reported? Critique and recommendations. [Journal Article]. \u003cem\u003eRes Nurs Health\u003c/em\u003e, 29(5), 489-497. doi: 10.1002/nur.20147\u003c/li\u003e\n\u003cli\u003eSchaye, V., \u0026amp; Triola, M. M. (2024). The generative artificial intelligence revolution: How hospitalists can lead the transformation of medical education. [Journal Article]. \u003cem\u003eJ Hosp Med\u003c/em\u003e, 19(12), 1181-1184. doi: 10.1002/jhm.13360\u003c/li\u003e\n\u003cli\u003eTavakol, M., \u0026amp; Dennick, R. (2011). Making sense of Cronbach\u0026apos;s alpha. [Editorial]. \u003cem\u003eInt J Med Educ\u003c/em\u003e, 2, 53-55. doi: 10.5116/ijme.4dfb.8dfd\u003c/li\u003e\n\u003cli\u003eThyssen, C., Huwer, J., Irion, T., \u0026amp; Schaal, S. (2023). From TPACK to DPACK: The \u0026ldquo;Digitality-Related Pedagogical and Content Knowledge\u0026rdquo;-Model in STEM-Education \u003cem\u003eEducation Sciences\u003c/em\u003e (13, pp.). (Reprinted.\u003c/li\u003e\n\u003cli\u003eWeidener, L., \u0026amp; Fischer, M. (2024). Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education. [Journal Article]. \u003cem\u003eJMIR Med Educ\u003c/em\u003e, 10, e55368. doi: 10.2196/55368\u003c/li\u003e\n\u003cli\u003eZhuang, H., Zhao, H., Wang, Y., He, C., Zhai, J.,... Wang, B. (2025). Development and validation of a school satisfaction scale for medical students. [Journal Article; Validation Study]. \u003cem\u003eBMC Med Educ\u003c/em\u003e, 25(1), 379. doi: 10.1186/s12909-025-06962-w\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of Pre-test Sample (n=30)\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"912\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Item\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Content\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Frequency\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Percentage (%)\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Cumulative Percentage (%)\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003eGender\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003eAge Group\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18-25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e31-40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003eEducation Level\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAssociate Degree (In Progress)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBachelor\u0026apos;s Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMaster\u0026apos;s Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003eMajor Category\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eClinical Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedical Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003eGenAI Application Experience\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNever used\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUsed 1-2 types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUsed 3-5 types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Demand Scale for Educator Competency in GenAI-Integrated Medical Simulation Education (16-80 points)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"916\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Category\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Item\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;1\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;2\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;3\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;4\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;5\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\"\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003eTraditional Medical Simulation Teaching \u0026amp; Literacy Competency\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e1. Solid Medical Expertise\u003c/strong\u003e\u0026zwnj;: Mastery of foundational and clinical medical knowledge; ability to accurately explain diseases.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e2. Rich Clinical Experience\u003c/strong\u003e\u0026zwnj;: Extensive clinical practice and case integration for teaching.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e3. Effective Teaching Methodology\u003c/strong\u003e\u0026zwnj;: Proficiency in diverse teaching methods and adaptive application.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e4. Strong Communication Skills\u003c/strong\u003e\u0026zwnj;: Clear and precise communication with students and patients, providing actionable guidance.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e5. Learning Facilitation\u003c/strong\u003e\u0026zwnj;: Guiding students to develop learning plans and autonomous study habits.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e6. Curriculum Design \u0026amp; Organization\u003c/strong\u003e\u0026zwnj;: Logical structuring of content and progress.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e7. Teaching Evaluation\u003c/strong\u003e\u0026zwnj;: Objective assessment of student outcomes and feedback delivery.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e8. Team Collaboration\u003c/strong\u003e\u0026zwnj;: Effective coordination with educators and healthcare professionals to foster student development.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e9. Professional Ethics \u0026amp; Humanistic Literacy\u003c/strong\u003e\u0026zwnj;: Adherence to medical ethics, patient-centered care, and exemplary conduct.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenAI-Technology Integration \u0026amp; Innovation Competency\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e10. GenAI Tool Proficiency\u003c/strong\u003e\u0026zwnj;: Skilled use of generative AI tools (e.g., ChatGPT, DeepSeek, DALL-E, medical diagnostic AI) for lesson preparation and case generation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e11. GenAI Content Verification\u003c/strong\u003e\u0026zwnj;: Ability to evaluate accuracy and reliability of AI-generated medical information.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e12. GenAI-Driven Curriculum Design\u003c/strong\u003e\u0026zwnj;: Leveraging AI to analyze student data and tailor personalized teaching plans.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e13. GenAI Ethics \u0026amp; Safety\u003c/strong\u003e\u0026zwnj;: Knowledge of ethical guidelines (e.g., data privacy, AI diagnostic accountability) for medical AI applications.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e14. GenAI-Guided Learning\u003c/strong\u003e\u0026zwnj;: Training students to use GenAI tools for knowledge acquisition and case analysis with critical thinking.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e15. GenAI Data Analysis\u003c/strong\u003e\u0026zwnj;: Interpreting AI-generated teaching data (e.g., student knowledge/skill metrics) to evaluate outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026zwnj;\u003cstrong\u003e16. GenAI-Teaching Integration Innovation\u003c/strong\u003e\u0026zwnj;: Pioneering novel methods (e.g., AI-enhanced simulation cases) to diversify pedagogy.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Input Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTest Family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCorrelation and regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUsed to analyze the correlation between scale items and total scores to determine sample size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistical Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCorrelation: Point - biserial correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAnalyzes the correlation between items (similar to binary variable characteristics) and scale total scores (continuous variables)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect Size (r)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAnticipated moderate correlation between items and scale total scores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistical Power (1\u0026minus;\u0026beta;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80% confidence in detecting true associations between items and total scores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSignificance Level (\u0026alpha;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTwo-tailed test, setting the probability of committing a Type I error at 5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of Variables (Items)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of items in the teaching ability scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026zwnj;\u003cstrong\u003eTable 4. Analysis Results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"987\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Reference Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistical Power (1\u0026minus;\u0026beta;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIdeal value \u0026ge; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eActual Effect Size (r)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3 (preset)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCritical r Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026plusmn;0.16 (df=418)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReference value for judging significant correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRequired Minimum Sample Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e418 individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Stratified Sampling Results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"989\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProfessional Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValid Questionnaires\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of Total Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIntern Proportion (Within Stratum)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of Individuals Within One Year of Graduation (Within Stratum)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eClinical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.3% (157/312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.7% (155/312)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.1% (34/64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.9% (30/64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedical Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.7% (30/58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.3% (28/58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: General Information (n=434)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"997\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Gender\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e189\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e43.55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e245\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Age Group\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18-25 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e356\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e26-30 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31-40 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41-50 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Education Level\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssociate Degree (In Progress)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBachelor\u0026apos;s Degree (In Progress)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e196\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssociate Degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBachelor\u0026apos;s Degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e122\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaster\u0026apos;s Degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;Major\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNursing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical Technology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Medicine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e312\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.89\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026zwnj;GenAI Application Experience\u0026zwnj;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNever used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e136\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsed 1-2 types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e220\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsed 3-5 types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsed 6 or more types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026zwnj;\u003cstrong\u003eTable 7. \u0026nbsp;Distribution of Total Scores (n=434)\u003c/strong\u003e\u0026zwnj;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"972\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimum (M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximum (X)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAverage (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8. \u0026nbsp;Reliability and Convergent Validity Results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eatent Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Manifest Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026apos;s Alpha\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTraditional Medical Simulation Teaching \u0026amp; Competency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGenAI Technology \u0026amp; Medical Simulation Teaching Integration Innovation Competency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9. Discriminant Validity Results\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"965\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLatent Variable 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSquare Root of AVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLatent Variable 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePearson Correlation Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTraditional Medical Simulation Teaching \u0026amp; Competency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGenAI Technology \u0026amp; Medical Simulation Teaching Integration Innovation Competency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"TPACK competency model, Generative Artificial Intelligence, medical simulation education, faculty competence, mixed methods research","lastPublishedDoi":"10.21203/rs.3.rs-6595296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6595296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e\n\u003cp\u003eThe rapid advancement of Generative Artificial Intelligence (GenAI) is reshaping the landscape of medical simulation education, necessitating the enhancement of faculty competencies to effectively integrate evolving knowledge systems with GenAI for collaborative decision-making. However, current educational technologies face systemic limitations, including fragmented functionality, a disconnect between conventional and GenAI-driven teaching approaches, and a lack of dynamic capability assessment tools. Constructing a standardized capability scale is crucial to overcoming adaptation bottlenecks.\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eGrounded in the integrated Technological Pedagogical Content Knowledge (TPACK) framework, this study employed a two-round Delphi method to construct a faculty competency assessment scale, with stratified sampling involving 434 participants from clinical medicine, medical technology, and nursing fields. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to test model fit (CFI=0.939, RMSEA=0.117). This study was not registered as a clinical trail.\u003c/p\u003e\n\u003cp\u003eResult：\u003c/p\u003e\n\u003cp\u003eThe resulting 16-item scale exhibited strong psychometric properties, demonstrating excellent internal consistency (Cronbach’s α = 0.979), sampling adequacy (KMO = 0.961), and significant sphericity (Bartlett’s test, p \u0026lt; 0.001). No factor covariance was detected, and item consensus was high (Kendall’s W = 0.761, p \u0026lt; 0.001). The model showed acceptable fit (χ²/df = 6.893).\u003c/p\u003e\n\u003cp\u003eConclusion：\u003c/p\u003e\n\u003cp\u003eThis validated and standardized assessment model offers a robust empirical tool to support the integration of GenAI into simulation-based medical education. It provides a foundational framework for advancing faculty competency development, thereby fostering adaptive, technology-enhanced teaching practices in the era of intelligent education.\u003c/p\u003e\n\u003cp\u003eTrial registration:\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Ethics Committee of Wenzhou Medical University Second Affiliated Hospital, with Ethical Approval Number: XY-2024-003. Data collection was conducted between February 14 and March 28, 2025.\u003c/p\u003e","manuscriptTitle":"Construction of a faculty competency model for medical simulation education integrated with GenAI: A mixed study based on the perspective of medical students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 14:59:08","doi":"10.21203/rs.3.rs-6595296/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-19T11:41:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T09:53:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-08T15:47:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139103928111807267963398067435104187511","date":"2025-09-29T13:54:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132257547017669906488432262089922667031","date":"2025-09-28T02:30:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245409150258063383041987179451437087121","date":"2025-09-25T11:01:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228819624934970910513703830861918359472","date":"2025-09-25T10:44:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-04T18:19:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-22T07:20:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-22T06:47:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-05-22T06:46:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3000e61-df87-40f9-bd06-974da9bbc820","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-15T10:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 14:59:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6595296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6595296","identity":"rs-6595296","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}