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Applying Artificial Intelligence in Physical Education and Future Perspectives

Author

Listed:
  • Hyun Suk Lee

    (Graduate School of Education, Chung Ang University, Seoul 06974, Korea)

  • Junga Lee

    (Faculty of Sports Medicine and Science, Kyung Hee University, Seoul 02447, Korea)

Abstract

Artificial intelligence (AI) is gradually influencing every aspect of everyday life, including education. AI can also provide special support to learners through academic sustainability or discontinuation predictions. While AI research remains in its early stages, we must examine how it evolves and exerts its potential over time. By utilizing AI in physical education (PE), we can increase its potential use in sports applications, and enact changes upon the nature of PE, its visualization, and repeatability. Based on the concept of AI and related research areas, this study explores its principles and use in PE, and presents a focused, in-depth analysis of the areas of PE technology where AI could be applied—customized PE classes, knowledge provision, learner evaluation, and learner counseling methods. Our findings highlight the expertise required for future PE teachers in applying AI. Regarding practice implications, this study addresses the topic of AI innovations affecting all life domains, including PE; it highlights AI applications’ relevance to PE technology, based on existing research; it proposes that the implications of AI for PE may apply to other educational domains; and finally, it contributes to existing literature and also shares future research prospects regarding AI applications in education and sports.

Suggested Citation

  • Hyun Suk Lee & Junga Lee, 2021. "Applying Artificial Intelligence in Physical Education and Future Perspectives," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:1:p:351-:d:473980
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    References listed on IDEAS

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    1. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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    Cited by:

    1. Junfeng Yang & Gaojun Shi & Wenjuan Zhu & Yao Sun, 2025. "Intelligent technologies in smart education: a comprehensive review of transformative pillars and their impact on teaching and learning methods," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
    2. Taofeng Liu & Dominika Wilczyńska & Mariusz Lipowski & Zijian Zhao, 2021. "Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform," IJERPH, MDPI, vol. 18(17), pages 1-13, August.
    3. Jun Chu, 2025. "The Application Trends and Challenges of Deep Learning and Flipped Classroom in Physical Education Teaching in Universities," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global Scientific Publishing, vol. 20(1), pages 1-18, January.
    4. Majdi Maabreh & Arwa Maabreh & Basheer Qolomany & Ala Al-Fuqaha, 2022. "The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights," International Journal of Distributed Sensor Networks, , vol. 18(7), pages 15501329221, July.
    5. Mrim M. Alnfiai & Faiz Abdullah Alotaibi & Mona Mohammed Alnahari & Nouf Abdullah Alsudairy & Asma Ibrahim Alharbi & Saad Alzahrani, 2025. "Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-18, December.
    6. Leilei Zhao & Xiaofan Wu & Heng Luo, 2022. "Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    7. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps," Sustainability, MDPI, vol. 13(11), pages 1-21, May.

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