Revolutionising Faculty Development and Continuing Medical Education Through AI-Generated Videos

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Digital Learning

Background:

• High-quality continuing medical education (CME) video production is resource-intensive.
• Generative AI presents a potential solution for efficient synthetic video creation.
• Evaluating AI platforms for educational content generation is crucial.

Purpose of the Study:

• To compare four generative AI video platforms (HeyGen, Synthesia, Colossyan, HourOne) for CME video production.
• To assess learner engagement and perception of AI-generated educational videos.
• To explore the necessity of disclosing AI-generated content in educational settings.

Main Methods:

• Comparative analysis of AI video generation platforms using the Kano model.
• Creation of two AI-generated educational videos.
• Collection of feedback from 25 learners on video quality, engagement, and AI recognition.

Main Results:

• HeyGen platform met most requirements for AI video generation.
• Learners perceived AI videos as good quality, engaging, and well-paced.
• Only 32% recognized videos as AI-generated, citing limitations in expressiveness and vocalization.
• 24% considered disclosure of AI-generated content necessary.

Conclusions:

• AI-generated videos are a viable, cost-effective alternative for CME content.
• Further development is needed to enhance realism (facial expressions, vocal tone).
• Ethical guidelines for AI content disclosure in education require careful consideration.