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 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 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.