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

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Publicado en:Smart Learning Environments vol. 12, no. 1 (Dec 2025), p. 52
Autor principal: Ma, Jiewei
Otros Autores: Ma, Lianzhen, Qi, Shilong, Zhang, Bo, Ruan, Wenpian
Publicado:
Springer Nature B.V.
Materias:
Feedback
Automation
Machine learning
Colleges & universities
Adaptive algorithms
Artificial intelligence
Education
Online instruction
Real time
Physical education
Adaptive learning
Literature Reviews
Independent Study
Competence
Guidance
Learning Motivation
Influence of Technology
Learning Processes
Learning Theories
Imitation
Blended Learning
Feedback (Response)
Databases
Access to Education
Electronic Learning
Instructional Effectiveness
Student Motivation
MOOCs
Educational Environment
Database Management Systems
Cognitive Ability
Learner Engagement
Cognitive Psychology
Educational Equity (Finance)
Algorithms
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2196-7091
DOI:10.1186/s40561-025-00411-3
Fuente:Education Database