Application Architecture and Future Trends of Generative Artificial Intelligence in Physical Education Teaching

DOI

10.2991/978-2-38476-523-2_8How to use a DOI?

Keywords

Generative artificial intelligence; Physical education teaching; Application architecture; Human-AI collaboration; Educational digital transformation

Abstract

Purpose: This study systematically explores the integration of generative artificial intelligence (AI) in physical education (PE) teaching, aiming to address challenges such as weak technological adaptability and lagging teacher digital literacy while establishing a sustainable intelligent PE ecosystem.

Methods: A mixed-methods approach was adopted, combining literature analysis, semi-structured interviews with 10 experts, dual-track surveys (480 PE students and 77 teachers), case studies of six representative AI-PE projects, and logical analysis guided by interdisciplinary theories.

Findings: Current integration of generative AI in PE teaching operationalizes through five functional dimensions: data collection, model training, evaluation, resource generation, and coaching assistance. Studies reveal teachers prioritize AI tools but lack VR integration skills, while students face technical and adaptive barriers. Case studies validate AI’s efficacy in data-driven decisions, immersive environments, and human-AI collaboration. This study proposes a novel four-layer architecture—Perception, Cognitive, Execution, Socio-Ethical—as its theoretical core, bridging technical implementation with pedagogical innovation.

Conclusion: Generative AI transforms PE teaching into a precise, immersive, and sustainable paradigm, driven by human-AI synergy, multimodal immersion, and ethical governance. Future trends emphasize adaptive lifelong learning ecosystems, advocating for balanced technological innovation and educational equity.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chaohui Lin
PY  - 2025
DA  - 2025/12/29
TI  - Application Architecture and Future Trends of Generative Artificial Intelligence in Physical Education Teaching
BT  - Proceedings of the 5th International Conference on New Media Development and Modernised Education (NMDME 2025)
PB  - Atlantis Press
SP  - 60
EP  - 78
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-523-2_8
DO  - 10.2991/978-2-38476-523-2_8
ID  - Lin2025
ER  -