Introduction to Fundamental AI and Ethical Considerations: A Didactic and Interactive Case-Based Session for Preclerkship Medical Students.

INTRODUCTION: Widespread integration of artificial intelligence (AI) into health care systems and evolving policies are altering how we must teach medical students. Fundamental AI knowledge and the ethical and responsible use of AI are currently suggested competency domains for health care professionals. Moreover, ethical AI disclosure is incorporated into developing institutional and state policies. Addressing this curricular gap, we created a session to teach preclinical medical students essential AI terminology, prompting strategies, and ethical considerations for AI use in patient care. METHODS: We implemented a 1-hour didactic lecture followed by a 2-hour case-based learning session in the 2025-2026 Medicine, Behavior, and Society module for 236 first-year medical students. We used a postsession, retrospective survey to assess students' perceived knowledge of the learning objectives. We used the Wilcoxon signed rank test (\ualpha = .05) to analyze survey response data. We developed a 5-question multiple-choice quiz (MCQ) to assess students' postsession knowledge and performed a psychometric item analysis. RESULTS: Ninety-seven (41.1%) participating students completed the postsession survey and quiz. Postsession perceived knowledge improved across all objectives, with a large (r > 0.50) effect size on a 5-point Likert scale. The mean (SD) postsession MCQ score was 3.77 (1.02), with discrimination indices ranging from 0.154 to 0.692. DISCUSSION: This combined didactic and case-based session delivers timely instruction on AI fundamentals and ethics for preclinical learners. The session significantly increased perceived knowledge, equipping students for future ethical and responsible AI use during clerkships.