Exploration and Application Research of DeepSeek in Physical Education Teaching

Abstract

In recent years, artificial intelligence technology has developed rapidly, prompting the field of physical education to transition from a traditional, experience-driven model to a new paradigm of data empowerment. This article comprehensively employs literature review and case analysis methodologies to systematically explore the underlying connotation, practical value, existing problems, and future strategies of artificial intelligence integration in physical education. The results indicate that utilizing advanced technologies, such as DeepSeek, for capturing and correcting classroom movements enables highly accurate feedback during technical movement instruction. Furthermore, integrating deep learning algorithms and big data analytics into personalized training prescriptions significantly enhances both teacher instructional efficiency and students' overall sports performance. By constructing a hybrid teaching model that combines real-time artificial intelligence analysis with expert classroom guidance, institutions can successfully realize a collaborative 'double-teacher' environment. However, the conclusion highlights that artificial intelligence educational applications currently face substantial challenges, including data privacy concerns, inherent algorithm bias, prohibitive equipment costs, and a general lack of artificial intelligence literacy among teaching staff. Moving forward, strategic efforts must be directed across four critical dimensions: robust national policy support, strict cultural and ethical guidelines, the development of cost-effective control equipment, and comprehensive teacher quality training. These initiatives will promote the sustainable development of artificial intelligence in physical education and provide a foundational paradigm for digital transformation in the modern educational era.