Addressing Clinician-Educator Hesitancy Toward Artificial Intelligence Through a Peer-Led Instructional Design.

INTRODUCTION

Artificial intelligence (AI) has had a tremendous impact on medical education. National data are indicative of massive clinical interest and usage, but a "faculty readiness gap" exists among experienced clinician-educators. Though closing this gap is essential, traditional training models often fail to improve appropriate application of AI tools. This study describes medical education development, innovation, and competency in AI (MEDIC-AI), an instructional method designed to increase faculty readiness through peer modeling of AI applications critical to success.

METHODS

A physician-educator (NW) at the Medical College of Georgia (MCG) at Augusta University created a peer-led instructional platform to explain and model the use and application of AI tools. The pilot episode was distributed anonymously to colleagues, and perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI) to implement at least one AI tool within 30 days were measured using a cross-sectional survey based on the Technology Acceptance Model (TAM).

RESULTS

Evaluations from 33 faculty demonstrated substantial acceptance, reporting high PU (90.9%) and PEOU (81.8%) regarding the format, and 87.9% demonstrated BI. Qualitative data suggest that peer explanation and modeling were critical to the results.

CONCLUSION

Using AI can simplify workflow for busy physician-educators. When AI applications are simply and concisely explained and demonstrated by a trusted colleague hosting a do-it-yourself (DIY) format, participants reported increased usefulness, ease of use, and intent to implement. This suggests that incorporation of peer leadership into traditional instruction on AI tool use has the potential to close the "faculty readiness gap," improving teaching and patient care.