A Training Needs Analysis for AI and Generative AI in Medical Education: Perspectives of Faculty and Students.

Abstract

Introduction: The growing presence of artificial intelligence (AI) in health professions has created a need to investigate its potential benefits and challenges in medical education. This article presents findings from an AI learner training needs analysis survey at a U.S. medical school. It compares faculty and student experiences and perspectives on using generative AI (GAI) and other AI tools for undergraduate medical education, focusing on their respective knowledge and learning preferences.

Methods: Faculty and students were surveyed using an online cross-sectional survey design to assess their GAI experience, AI patterns of use, adoption readiness, and training preferences. Surveys contained 14 to 15 multiple-choice items, with 8 items including a write-in option. A total of 68 faculty and 506 students responded to the survey, with a 50% response rate for faculty and 30% for students. Statistical tests were used to determine whether students and faculty differed significantly in their GAI experience.

Results: We found that students were significantly more familiar with GAI than faculty (P < .001) but not significantly more experienced with GAI tools. There were no significant differences in frequency of use. Both groups considered AI tools and technology useful for personal, academic, research, and clinical applications. More than half of both groups were using AI for academic tasks. Both groups expressed concerns about the reliability of AI output, with faculty showing a much greater level of concern. Both groups identified several training formats as beneficial, with faculty preferring formal training (either online or in-person), followed by peer tutorials and self-study. On the other hand, students showed slightly greater interest in self-study than other formats.

Conclusion: Our findings will inform the design of two parallel structured AI training programs, focusing on faculty and student priorities, including hands-on skills practice, and emphasizing AI's ethical use, reliability, and limitations.