Feasibility of artificial intelligence-driven personalized learning for internal medicine residents: Integrating adaptive artificial intelligence in flipped classrooms

Medical residency training faces persistent challenges in delivering individualized learning experiences. While flipped classroom models promote engagement, they often lack real-time, personalized feedback. Artificial intelligence (AI)-driven platforms offer a promising solution by dynamically adapting content to residents’ evolving needs. This study evaluated the feasibility and effectiveness of integrating adaptive AI beings into a flipped classroom model for internal medicine residents. The AI-powered platform, edYOU, incorporated a personalized ingestion engine to customize learning content and an intelligent curation engine to ensure content integrity. Residents interacted with AI beings capable of adjusting real-time content delivery based on performance and progress. Learning outcomes were assessed using platform engagement metrics, simulation-based quiz results, and resident feedback. Among eligible residents, 92% actively used the platform, spending an average of 32.3 h (a few minutes to 148 h). A significant positive correlation was observed between time spent on the platform and quiz performance (r = 0.63, p<0.001), with 82.6% of educational topics engaged.