Artificial Intelligence (AI)-Based simulators versus simulated patients in undergraduate programs: A protocol for a randomized controlled trial.

Background: Healthcare simulation is critical for medical education, with traditional methods using simulated patients (SPs). Recent advances in artificial intelligence (AI) offer new possibilities with AI-based simulators, introducing limitless opportunities for simulation-based training. This study compares AI-based simulators and SPs in undergraduate medical education, particularly in history-taking skills development.

Methods: A randomized controlled trial will be conducted to identify the effectiveness of delivering a simulation session around history-taking skills to 67 fifth-year medical students in their clinical years of study. Students will be assigned randomly to either an AI-simulator group (intervention) or a simulated patient group (control), both will undergo a history-taking simulation scenario. An Objective Structured Clinical Examination (OSCE) will measure the primary outcomes. In contrast, secondary outcomes including student satisfaction and engagement, will be evaluated following the validated Simulation Effectiveness Tool-Modified (SET-M). The statistical approach engaged in this study will include independent t-tests for group performance comparison and multiple imputations to handle missing data.

Discussion: This study's findings will provide valuable insights into the comparative advantages of artificial intelligence-based simulators and simulated patients. Results will guide decisions regarding integrating AI-based simulators into healthcare education and training programs. Hybrid models might be considered by institutions in the light of this study, providing diverse and effective simulation experiences to optimize learning outcomes. Furthermore, this work can prepare the ground for future research that addresses the readiness of AI-based simulators to become a core part of healthcare education.