Integrating Task-Technology Fit and Community of Practice Theories to Enhance Medical Educator Skill Confidence in...

Area of Science:

• Medical Education
• Artificial Intelligence in Education
• Faculty Development

Background:

• Traditional faculty development for generative AI (genAI) is often unsustainable or resource-intensive.
• Medical educators require practical, ongoing approaches to integrate genAI effectively.
• Existing methods like workshops lack long-term impact and scalability.

Purpose of the Study:

• To pilot a sustainable faculty development model for generative AI (genAI) using a community of practice (CoP).
• To integrate task-technology fit (TTF) principles and social learning into faculty genAI training.
• To assess the impact of the CoP on medical educators' genAI knowledge and confidence.

Main Methods:

• A six-session community of practice (CoP) was delivered to medical education faculty.
• The CoP focused on authentic micro-tasks with demonstrated AI-task alignment, guided by TTF principles.
• Two facilitators led the sessions, and 13 participants completed post-series surveys.

Main Results:

• Significant increase in self-rated genAI knowledge (p < 0.001).
• Strong correlation between skill confidence and attendance (r=0.78) and a welcoming environment (r=0.68).
• Most participants found content highly relevant and planned immediate application.

Conclusions:

• A TTF-informed CoP model offers a sustainable and effective approach for faculty genAI development.
• Integrating task-specific alignment and community support rapidly enhances educators' genAI confidence.
• This theory-based model provides an easily adoptable solution for ongoing faculty training in genAI.