What and When: AI Learning Objectives and Competencies for Medical Students

Introduction: This study aimed to define AI-related competencies and learning objectives (LOs) that medical students should acquire during undergraduate medical education following the advent of publicly available large language models. We also propose guidelines for integrating these competencies into curricula. Methods: A four-round Delphi process was conducted between May 2024 and January 2025. The panel included medical students (n = 14) and medical professors (n = 2) in the first three rounds, and international experts in medical education and AI in the final round (n = 10). In Round 1, using an open-ended question, 78 potential LOs were identified. In Round 2, these items were rated using a 7-point Likert scale (1 = Strongly Disagree, 7 = Strongly Agree), and participants were also provided an area for comments. Median scores and interquartile ranges were used to determine the strength of agreement, and the LOs were further modified based on this. Round 3 followed a similar process. In Round 4, input was sought from 10 international experts in medical education and AI. The learning objectives were then organized into categories representing broader competencies. Results: The process identified 7 competencies and 37 supporting LOs essential for medical graduates. In addition, timelines for addressing these competencies and LOs are also proposed. Conclusion: This study proposes a framework for integrating AI into undergraduate medical education, combining a theoretical understanding of AI with its ethical use to prepare future physicians for clinical practice. Developed with multinational input, these competencies and LOs may have international relevance.