Trends and Applications of Artificial Intelligence in Competency-Based Education in Medical Programs: A Scoping Review

Introduction: The integration of Artificial Intelligence (AI) in medical education has emerged as a transformative shift, particularly within Competency-Based Medical Education (CBME). AI technologies, including Natural Language Processing (NLP) and machine learning, offer opportunities to enhance personalized learning and competency assessment.

Methods: A scoping review was conducted following the framework by Arksey and O'Malley (2005) to examine the current integration of AI in CBME. Empirical studies were included, focusing on AI applications in medical education, competency assessments, and skill development.

Results: The 50 studies, published from 2010 to 2025, were included in the scoping review and the synthesized evidence demonstrated that AI has shown potential in automating assessments, providing real-time feedback, and supporting personalized learning paths. Common AI technologies such as generative AI, NLP, and machine learning were applied across diverse medical education settings. However, challenges regarding ethical concerns, faculty training, and limited integration within established curricula were identified.

Conclusion: The integration of AI into CBME offers significant potential in medical education; however, several challenges remain. There is a need for more empirical research, longitudinal studies, and AI literacy programs such as training in prompt engineering, AI ethics, and responsible data use for both educators and students. Addressing these gaps will ensure AI’s effective, ethical, and equitable integration in medical training.