Integrating AI into undergraduate medical education: an exploration of learner-centered approaches through AI

Abstract

Introduction – Advances in artificial intelligence (AI) offer promising opportunities to enrich medical education by supporting self-directed learning, providing personalized feedback, and simulating clinical and scientific reasoning. Although AI use has expanded in graduate medical education for clinical decision training and administrative support, its effectiveness in undergraduate medical education remains insufficiently established. Empirical data examining how AI instruction influences academic performance, knowledge consolidation, engagement, and critical thinking are limited. This study evaluated whether incorporating AI-patient-based simulations into classroom activities enhances learning outcomes and student experience within an integrated osteopathic medical school curriculum. Methods – Using a mixed-methods survey design, quantitative and qualitative data were collected and analyzed to examine students’ perceptions of faculty-developed AI-patient-based simulations integrated into classroom instruction. Results – Students reported that AI-patient-based simulations supported engagement, reinforced learning, and facilitated application of clinical reasoning concepts. Qualitative findings suggested that the simulations promoted active participation and encouraged self-directed learning while highlighting the importance of faculty guidance in interpreting AI-generated content. Discussion – Overall, findings suggest that AI-patient-based simulations can provide meaningful support to medical student learning when thoughtfully integrated into the curriculum and accompanied by explicit instruction regarding limitations, ethical considerations, and responsible AI use. Further research is needed to determine the long-term effects of AI-assisted instruction on academic performance and critical thinking development.