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Background <p>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 standardised capability scale is crucial to overcoming adaptation bottlenecks.</p> Methods <p>Grounded in the integrated Technological Pedagogical Content Knowledge (TPACK) framework, this research proposed 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 proposed research was not registered as a clinical trial.</p> Results <p>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, <i>p</i> &lt; 0.001). No factor covariance was detected, and item consensus was high (Kendall’s W = 0.761, <i>p</i> &lt; 0.001). The model showed acceptable fit (χ²/df = 6.893).</p> Conclusion <p>This empirically validated and standardised assessment model provides robust support for integrating GenAI with simulation-based health sciences education. It offers a foundational framework for advancing faculty competency development and fostering adaptive, technology-enhanced teaching practices, while noting that the scale primarily captures ethical awareness rather than comprehensive governance competence.</p> Trial Registration <p>Not applicable. As this research did not involve clinical interventions, trial registration was not required.</p>

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Construction of a faculty competency model for medical simulation education integrated with GenAI: a multi-method quantitative study based on the perspective of medical students

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月20日

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  • Ying Zhu-ge,
  • Xin-tong Yao,
  • Hong-Xia Mei,
  • Hong-xiang Yao,
  • Qi Chen

摘要

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 standardised capability scale is crucial to overcoming adaptation bottlenecks.

Methods

Grounded in the integrated Technological Pedagogical Content Knowledge (TPACK) framework, this research proposed 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 proposed research was not registered as a clinical trial.

Results

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 empirically validated and standardised assessment model provides robust support for integrating GenAI with simulation-based health sciences education. It offers a foundational framework for advancing faculty competency development and fostering adaptive, technology-enhanced teaching practices, while noting that the scale primarily captures ethical awareness rather than comprehensive governance competence.

Trial Registration

Not applicable. As this research did not involve clinical interventions, trial registration was not required.