Artificial intelligence for university physical education: a data-knowledge synergy digital-intelligent sports platform.

This study presents the design, construction, and preliminary evaluation of a university-oriented digital-intelligent sports platform integrating a large language model with retrieval-augmented generation (RAG), campus data services, and domain knowledge resources. The platform architecture comprises technical, knowledge, operational-data, and user-application components supporting teaching assistance, exercise guidance, competition organization, and health-related information services. A formative campus deployment involving 428 students and six instructors documented implementation feasibility and user-facing application scenarios. To provide a more rigorous empirical supplement, an independent six-week, non-randomized, intact-class quasi-experiment was conducted with 196 students in four physical-education classes. Two classes received platform-supported instruction and two received conventional instruction from the same instructor. Baseline-adjusted analyses indicated higher posttest basic psychological need satisfaction in the platform-supported group (adjusted difference = 0.618, 95% CI 0.506-0.730, p < 0.001) and lower need frustration (adjusted difference = -0.653, 95% CI -0.759 to -0.547, p < 0.001). The adjusted difference in self-reported physical activity was not statistically significant (p = 0.216). These findings provide preliminary evidence of more favorable psychological-need experiences during platform-supported physical education, but do not indicate a corresponding increase in self-reported physical activity over 6 weeks. The central contribution lies in the education-oriented integration of AI architecture, institutional knowledge resources, and classroom implementation evidence within a unified university physical-education platform.