Perceived Ratings of AI-Generated and Expert-Reviewed vs Clinician-Developed Virtual Cardiology Patient Scenarios Among Medical Students: Randomized Controlled Study

Virtual patients (VPs) are widely used in medical education, but creating high-quality cases requires clinical expertise, iterative revision, and alignment with educational objectives. Generative AI may support VP authoring, but direct comparisons between AI-generated and clinician-developed cases delivered within the same educational setting remain limited.

This study aimed to compare medical students' learner-perceived ratings of 1 AI-generated, expert-reviewed virtual cardiology patient scenario with those of 1 clinician-developed virtual cardiology patient scenario delivered through the same mobile platform.

A randomized, participant-blinded 2-arm controlled study was conducted in October 2025 during scheduled fifth-year emergency medicine course sessions. Eligible participants were fifth-year medical students who had completed the cardiology course. Students were assigned 1:1 to complete either a clinician-developed VP (CD-VP; n=64) or a ChatGPT-generated VP (GPT-VP; n=67). Both scenarios followed a shared acute coronary syndrome blueprint and were delivered through the same mobile-optimized app (mobile VPs app [MobiViP]) with identical interface structure and branching logic. The GPT-VP was generated using GPT-5 and refined through iterative follow-up prompting under expert review; direct manual rewriting was prohibited. After completion, students completed a mandatory 19-item questionnaire covering realism and validity, cognitive load, self-reported learning and reflection, and engagement and satisfaction. Analyses included reliability testing, 1-sample tests against a neutral midpoint, Mann-Whitney U tests, and Spearman correlations. Full scenario content and development details are provided as multimedia appendices.