A089: Value Reconstruction and Practical Dimensions of AI-Empowered Reform in University Physical Education

Driven by educational digital transformation and the iterative advancement of artificial intelligence (AI), university physical education is undergoing a paradigm shift from an "experience-driven" to a "data-driven" model. Method: Based on the theoretical framework of digital educational transformation, this study systematically explores the value positioning, technical pathways, and practical challenges of AI in reforming physical education. Artificial Intelligence intervenes in pedagogical change through three important domains and reveals multiple realities of obstruction in current practice: Instructional Mode: By leveraging intelligent perception and learning analytics technologies, AI establishes personalized exercise prescription systems, transitioning from standardized teaching to precision-guided instruction. Evaluation System: Integrating computer vision and biomechanical dynamic process diagnostics, AI develops dynamic skill assessment models, shifting summative assessments to developmental diagnostics. Resource Management: Utilizing digital twin and virtual simulation technologies, AI transcends spatial-temporal constraints of traditional teaching, creating smart physical education environments that enhance embodied cognition and contextualized learning experiences. Through constructing a closed-loop system encompassing "data collection-intelligent decision-making-feedback," AI significantly improves injury prevention effectiveness, optimizes skill acquisition pathways, and expands ideological-political educational integration within physical education. Artificial intelligence intervenes in three dimensions: educational subject, process, and space to realize the reform of physical education teaching in colleges and universities. The study further identifies critical implementation barriers: technical incompatibility arising from industrial algorithms misaligned with educational needs, governance challenges in ethics due to inadequate protection of sports data privacy, and diminished technical efficacy caused by insufficient teacher AI literacy. To address these issues, future reforms should prioritize three collaborative dimensions: developing lightweight educational-specific algorithm models, establishing multi-stakeholder data governance mechanisms, and reshaping teacher roles through "digital coach" training systems. The deep integration of AI and physical education transcends mere instrumental innovation, representing an intelligent return to the essence of "holistic development through physical education." Its advancement must adhere to pedagogical primacy, fostering symbiosis between disciplinary knowledge and digital intelligence through technological innovation. This process necessitates balancing algorithmic precision with educational complexity, harmonizing data-driven rigor with the artistry of pedagogy, and integrating technological empowerment with the cultivation of sports literacy.