A practical study of artificial intelligence-based real-time feedback in online physical education teaching

Abstract To address poor skill acquisition in online physical education due to a lack of real-time feedback, we developed and evaluated a pose recognition-based system. An 8-week randomized controlled trial study in a university Baduanjin course compared the AI system against a traditional Massive Open Online Course format. Results showed the system significantly enhanced students' movement quality, fluency, learning interest, and self-directed learning. Crucially, mediation analysis identified increased learning duration as the primary significant mechanism driving this skill acquisition, outweighing changes in interest or self-direction within our model. While promising, the technology has limitations in accuracy and interactivity. Future research should focus on optimizing algorithms and integrating adaptive learning to create more effective OLPE strategies.