<p>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&#xa0;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, <i>p</i> &lt; 0.001). Skill confidence correlated strongly with attendance (<i>r</i> = 0.78) and welcoming environment (<i>r</i> = 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.</p>

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

  • Youngjin Cho,
  • John L. Szarek

摘要

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.