Theoretical evolution of AI in medical education: models, frameworks, and future directions

Background Artificial intelligence (AI) is increasingly reshaping medical education through personalized learning, adaptive assessments, and advanced simulations. This systematic narrative review synthesizes the theoretical development of AI in medical training, focusing on educational models, frameworks, learning outcomes, and stakeholder considerations. A literature search of PubMed, Scopus, Web of Science, and Google Scholar (January 2000–March 2025) identified 1,288 records, of which 48 studies met the inclusion criteria and were included in qualitative thematic synthesis. No statistical meta-analysis was conducted due to methodological heterogeneity. Results Five major AI domains emerged: Intelligent Tutoring Systems, Simulation-Based Medical Education, Adaptive Learning, Generative AI, and Explainable AI. These domains align with established instructional theories and contribute to improved engagement and learning efficiency. However, concerns persist regarding learner deskilling, academic integrity, and algorithmic bias. AI integration influences multiple stakeholders, including trainees, educators, clinicians, policymakers, and patients. The field has progressed from rule-based approaches to data-driven machine learning models, enabling personalized instruction. Responsible implementation necessitates addressing pedagogical, ethical, and practical challenges, while also reducing the global digital divide. Conclusions This systematic review provides guidance for educators, researchers, and policymakers on integrating AI effectively and ethically into medical education.