Research on the Mechanism and Application of Generative Artificial Intelligence Enabling Precise Teaching in Middle School Physical Education

Abstract

Generative AI is moving fast, and it’s starting to offer fresh ways to solve an old problem in middle school PE: how do you balance large class teaching with personalized development? This paper looks at exactly how generative AI can support and improve precise instruction in middle school physical education. We used a mix of methods—reviewing the literature, logical analysis, and looking at real world cases—to dig into the theoretical foundations, the main ways this technology works, and what it actually looks like in practice. The results show that:Generative AI enables more precise PE teaching through three connected mechanisms: personalized lesson design, real time feedback and analysis, and adaptive learning progression. The real engine behind all this is a human AI collaboration model built on the idea of “augmentation, not replacement.” In everyday practice, AI is already changing how teachers allocate resources, how students and teachers interact, and how learning outcomes are assessed. Of course, it’s not all smooth sailing. There are practical hurdles—worries about data privacy, the risk of algorithmic bias, the chance that teachers or students become too dependent on the technology, and the digital divide that leaves some schools behind. To get past these issues, we propose several targeted strategies: build a stronger theoretical framework for digital PE instruction, develop a clear human AI collaborative teaching model, tighten up the ethical rules for using AI, and boost teachers’ digital skills. Our hope is that this work offers both a theoretical reference and some practical guidance for reforming middle school PE in today’s intelligence driven era.