Generative AI–Enabled Reconstruction of University General Physical Education: Application Scenarios, Mechanisms, and Risk Boundaries

Abstract

The rapid development of generative artificial intelligence (GenAI) is moving higher education beyond the digitization of resources toward interactive, personalized, and intelligent learning support. Although GenAI has been widely discussed in language learning, writing, engineering, and medical education, university general physical education (PE) has distinctive characteristics, including embodied practice, motor-skill acquisition, real-time interaction, and safety risks. These features make it inappropriate to transfer generic "AI-assisted teaching" models directly into PE. Drawing on conceptual analysis and an integrative narrative review, this article examines how GenAI can be incorporated into university general PE without weakening embodied practice, teacher professional judgment, or student agency. It synthesizes literature on GenAI in higher education, artificial intelligence in PE, social cognitive theory, self-regulated learning, and human-centered AI, and shifts the central question from what the technology can do to what role it should play in PE. The article identifies appropriate uses in pre-class diagnosis and preparation, low-risk in-class knowledge support, differentiated task design, post-class reflection, and support for autonomous physical activity. On this basis, a Teacher–GenAI–Student collaborative model is proposed. The model assigns professional judgment, safety control, and formal evaluation to teachers; low-risk cognitive support such as explanation, goal decomposition, task generation, and structured reflection to GenAI; and embodied practice and learning decisions to students. Four potential mechanisms—differentiated support, PE learning self-efficacy, learning engagement, and self-regulation—are further identified, together with governance boundaries concerning hallucinations, physical safety, privacy, algorithmic bias, cognitive offloading, and teacher AI literacy. The article contributes a stage-specific framework that positions GenAI as a scaffold around physical practice rather than as a substitute for it. Its educational value lies in extending support across time and space while preserving teacher professionalism and students’long-term capacity for autonomous physical activity.