Integrating Task-Technology Fit and Community of Practice Theories to Enhance Medical Educator Skill Confidence in Generative AI: Design, Implementation, and Pilot Outcomes

TL;DR

A community-based approach using task-technology fit principles significantly boosted medical educators' confidence in using generative AI.

Contribution

Integrates task-technology fit and community of practice theories to create a sustainable AI training model for educators.

Findings

Self-rated knowledge increased by 1.3 points with p < 0.001.

Skill confidence correlated with attendance (r = 0.78) and a welcoming environment (r = 0.68).

Most participants found the content highly relevant and planned immediate use.

Abstract

Faculty need sustainable generative AI (genAI) development approaches beyond one-off workshops or resource-intensive formal programs. We piloted a community of practice (CoP) integrating task-technology fit (TTF) principles with social learning. Two volunteer facilitators delivered six sessions to medical education faculty, focusing on authentic micro-tasks with demonstrated AI-task alignment. Thirteen participants completed post-series surveys. Self-rated knowledge increased significantly (1.3 points, p < 0.001). Skill confidence correlated strongly with attendance (r = 0.78) and welcoming environment (r = 0.68). Most rated content as highly relevant and planned immediate use. TTF-informed CoP design rapidly increased educators’ genAI confidence through task-specific alignment and community support, offering a theory-based model for easily adoptable faculty development.

The online version contains supplementary material available at 10.1007/s40670-025-02520-7.