Challenges in the Rapid and Responsible Integration of Generative Artificial Intelligence (AI) Into a New Medical School Curriculum

Abstract

This study describes a systematic approach to integrate generative artificial intelligence (AI) into our new medical education curriculum at the Quillen College of Medicine while maintaining academic integrity. As medical schools navigate the widespread adoption of large language models, we implemented a five-step process to address the policy recommendations of the Association of American Medical Colleges (AAMC) on AI integration. First, surveys assessed AI usage patterns among students, revealing increasing adoption (from 24% to 77%) between May 2024 and February 2025. Second, clear professionalism guidelines were established, prohibiting AI use in generating learning objectives, writing Subjective, Objective, Assessment, and Plan (SOAP) notes, or completing assignments while permitting its use in research applications or otherwise in specific course settings when given permission to do so by faculty. Third, an institutional grant ensured equitable access to AI platforms for all students entering in Fall 2024. Fourth, AI was integrated into controlled educational settings, particularly within problem-based learning (PBL) for first-year students and team-based learning (TBL) for second-year students, with structured evaluation criteria. Finally, specialized training on ethical AI use was provided to students transitioning to clinical clerkships. Survey data indicated that students found AI exposure beneficial (91% agreement) and helpful for researching learning objectives (94% agreement), though confidence in AI's accuracy was lower (85% agreement). Students prioritized summarizing learning materials and testing understanding as important AI applications while valuing the ability to function as clinicians both with and without AI. Our approach demonstrates a balanced integration strategy that encourages responsible AI adoption while maintaining educational integrity in medical training.

Ähnliche Arbeiten