Generative AI Factors, Cognitive Engagement, and Patient Safety Learning in Medical Education: Mixed Methods Study

Generative AI (GenAI) is increasingly used during medical training, including clerkships in which learning takes place alongside supervised patient care. Its educational value may depend on how students interpret, verify, and regulate AI-generated output.

This study explored medical clerkship students' accounts of GenAI use and examined how GenAI-related factors were associated with self-reported ward-based infection-prevention practice through self-efficacy in AI-generated usage and perceived AI self-control balance.

We used an exploratory sequential mixed methods design. Eight medical clerkship students with experience using GenAI participated in semistructured interviews analyzed using reflexive thematic analysis. The qualitative findings were considered alongside theory and prior evidence to specify a theory-based model. This model was subsequently examined through an online cross-sectional survey of 214 eligible students recruited from 5 private universities in the Jakarta Metropolitan Area, Indonesia. Quantitative associations were analyzed using partial least squares structural equation modeling with bootstrapping.

The interviews yielded 16 themes concerning appraisal of AI-generated output, confidence in using GenAI, interaction control, patient-safety awareness, and infection-prevention learning. The structural paths were unchanged, although 3 low-loading measurement indicators were removed before hypothesis testing. Global model fit was limited (estimated-model standardized root mean square residual 0.089; normed fit index 0.691), and pairwise residual fit was not used as a retention criterion. Explainability quality exposure and perceived algorithmic fairness were positively associated with both self-efficacy and perceived AI self-control balance. Trust calibration literacy was associated with self-efficacy but not perceived AI self-control balance. Privacy concern was positively associated with self-efficacy (β=0.294, P<.001), contrary to the hypothesized direction, but not with perceived AI self-control balance. Self-efficacy (β=0.477, P<.001) and perceived AI self-control balance (β=0.407, P<.001) were positively associated with self-reported ward-based infection-prevention practice; the model explained 51.9% of its variance. Specific indirect effects through self-efficacy were observed for all 4 antecedents and through perceived AI self-control balance only for explainability and fairness. A small positive interaction was observed between promotion of patient safety and self-efficacy (β=0.122, P=.04); the corresponding interaction involving perceived AI self-control balance was not observed.

Self-efficacy was the more consistent pathway linking students' appraisals of GenAI with self-reported ward-based infection-prevention practice. By integrating students' accounts with quantitative testing of a theory-based model, this study extends research that has largely focused on GenAI acceptance and general educational use by examining a safety-oriented outcome within clinical training. The findings add evidence on the human factors through which GenAI use may relate to patient-safety learning, although the cross-sectional, self-reported data do not establish causal or clinical effects. Medical schools and teaching hospitals could use these findings to inform clerkship guidance on supervised verification of AI-generated output, patient-data protection, alignment with local protocols, and continued human responsibility for clinical decisions.