Controlled experiment demonstrates the efficacy of an AI and VR immersive training framework in basketball education, suggesting enhanced motion analysis and skill consolidation.
Traditional basketball specialized skills teaching focuses on teacher demonstration, manual error correction, and offline practical operation as the core mode. There are pain points such as insufficient standardization of action demonstration, difficulty in visualizing difficult actions, limited coverage of personalized error correction, restricted training scenarios, and low efficiency of skill consolidation, which make it difficult to meet the refined and differentiated needs of campus sports and specialized training. The deep integration of AI and VR immersive technology, relying on core capabilities such as 3D scene reconstruction, human pose capture, intelligent motion analysis, virtual simulation training, and real-time interactive feedback, provides a new path for basketball teaching that utilizes digitally simulated environments and automated biomechanical analysis to facilitate data-driven pedagogical feedback. This article takes basketball specialized teaching as the research object, systematically explains the technical architecture, core principles, and teaching adaptation logic of AI+VR immersive teaching, and constructs a full process application mode from five scenarios: basic skills, advanced skills, offensive and defensive confrontation, team tactics, and post class consolidation; Design a quantitative evaluation system that includes five dimensions: skill mastery, teaching efficiency, subjective experience, practical application, and long-term consolidation. Conduct empirical analysis through a 16 week controlled experiment. Motion-capture and sensing modules make the framework compatible with wearable motion-monitoring systems in physical education.
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S. H. (2026) studied this question.
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