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. Learners spent more time on difficult content areas, highlighting the system’s ability to adapt to individual challenges. Integrating AI into the flipped classroom proved feasible and was associated with improved engagement, learning efficiency, and academic performance. These results support using AI-enhanced educational tools to foster tailored, learner-centered experiences in graduate medical education. Further research is warranted to optimize implementation strategies and evaluate the long-term impact of AI-driven learning environments on resident development and competency outcomes.