Methodological study demonstrates improved exercise precision and health management in college students through an AI framework, highlighting the value of adaptive physical education.
Driven by educational digitalization and the Healthy China 2030 initiative, university physical education is increasingly expected to provide personalized instruction and precise health interventions. To address the limitations of conventional teacher-centered teaching models, this study investigates the paradigm transformation of physical education empowered by artificial intelligence. An intelligent closed-loop framework integrating data acquisition, health assessment, personalized intervention, and dynamic feedback is established. Wearable sensing devices and intelligent analytics are employed to monitor exercise load, physical fitness indicators, and learning behaviors in real time. AI-based recommendation algorithms are then utilized to generate adaptive exercise strategies and individualized training pathways. The proposed framework improves teaching precision, student engagement, and health-management effectiveness. The study provides methodological support for intelligent sports education and offers references for wearable sensing, physiological signal analysis, and intelligent health-monitoring systems.
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Y. Xu (2026) studied this question.
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