AI-Enhanced Sports Training in Physical Education: Global Research Trends, Pedagogical Methods, and Ethical Frameworks (2002–2025)
DOI:
https://doi.org/10.17309/tmfv.2026.2.03Keywords:
artificial intelligence in physical education, bibliometric analysis, physical education pedagogy, motor learning, teacher educationAbstract
Background. Artificial intelligence (AI) is rapidly entering physical education and sports training, providing new avenues for motion observation, learning diagnosis, and feedback. However, the pedagogical orientation and classroom feasibility of related research remain obscured by narratives focused primarily on technological performance.
Objectives. This study reviews and characterizes the knowledge base, thematic structure, and evolutionary trajectory of global AI-enabled physical education research from 2002 to 2025. It highlights implications for physical education decision-making and ethical governance, emphasizing the principle of “assisting teachers rather than replacing them.”
Materials and Methods. A Web of Science Core Collection search conducted in April 2025 yielded 729 peer-reviewed English-language journal articles. Co-citation networks, keyword co-occurrence analysis, and BERT-based semantic clustering were used to identify core literature, thematic clusters, and stages of development. These results were integrated with an interpretive synthesis from a physical education perspective.
Results. Research has evolved from early motion quantification and data-driven analysis to deep learning-based motion recognition and generative AI-supported instructional feedback, demonstrating a shift toward “instructional-ethical governance.” However, the evidence primarily focuses on universities and elite training programs. Empirical evidence in K–12 classrooms remains limited, and replicable implementation processes are lacking, creating a “laboratory–classroom” transition gap. Furthermore, issues such as curriculum articulation, learning stages, feedback timing, classroom risk management, and privacy and fairness remain insufficiently addressed.
Conclusions. In physical education, AI is best positioned as a teacher-led decision-support tool that enhances observation, formative assessment, and diagnostic feedback. Teachers should retain responsibility for key instructional judgments, prioritizing athletic development while ensuring safety and fairness. This study recommends using teaching effectiveness as the primary evaluation benchmark to promote explainable, controllable, and compliant classroom implementation pathways.
Downloads
References
Lee, H.S., & Lee, J. (2021). Applying artificial intelligence in physical education and future perspectives. Sustainability, 13(1), 351. https://doi.org/10.3390/su13010351 DOI: https://doi.org/10.3390/su13010351
Zhang, Y., Duan, W., Villanueva, L.E., & Chen, S. (2023). Transforming sports training through the integration of Internet technology and artificial intelligence. Soft Computing, 27(20), 15409-15423. https://doi.org/10.1007/s00500-023-08960-w DOI: https://doi.org/10.1007/s00500-023-08960-w
Lin, K.C., Cheng, I.L., Huang, Y.C., Wei, C.W., Chang, W.L., Huang, C., & Chen, N.S. (2023). The effects of the badminton teaching-assisted system using electromyography and gyroscope on learners’ badminton skills. IEEE Transactions on Learning Technologies, 16(5), 780-789. https://doi.org/10.1109/TLT.2023.3292215 DOI: https://doi.org/10.1109/TLT.2023.3292215
Chen, K., Cao, F., Hao, L., Xiang, M., & Kamruzzaman, M.M. (2022). Application analysis of digital neural network-based data mining method in maximizing the performance of sports training. Revista Brasileira de Medicina do Esporte, 29, e2022_0152. https://doi.org/10.1590/1517-8692202329012022_0152 DOI: https://doi.org/10.1590/1517-8692202329012022_0152
Rahim, N.I. M., Iahad, N.A., Yusof, A.F., & Al-Sharafi, M.A. (2022). AI-based chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-neural network modelling approach. Sustainability, 14(19), 12726. https://doi.org/10.3390/su141912726 DOI: https://doi.org/10.3390/su141912726
Zhao, X. (2022). Research on athlete behavior recognition technology in sports teaching video based on deep neural network. Computational Intelligence and Neuroscience, 2022, 7260894. https://doi.org/10.1155/2022/7260894 DOI: https://doi.org/10.1155/2022/7260894
Jiang, X., Du, Y., & Zheng, Y. (2024). Evaluation of physical education teaching effect using random forest model under artificial intelligence. Heliyon, 10(1), e23576. https://doi.org/10.1016/j.heliyon.2023.e23576 DOI: https://doi.org/10.1016/j.heliyon.2023.e23576
Kong, S.C., & Song, Y.J. (2015). An experience of personalized learning hub initiative embedding BYOD for reflective engagement in higher education. Computers & Education, 88, 227-240. https://doi.org/10.1016/j.compedu.2015.06.003 DOI: https://doi.org/10.1016/j.compedu.2015.06.003
Ma, J.J., Wang, P.C., Li, B.Q., Wang, T., Pang, X.S., & Wang, D.K. (2025). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human-Computer Interaction, 41(2), 1431-1445. https://doi.org/10.1080/10447318.2024.2314358 DOI: https://doi.org/10.1080/10447318.2024.2314358
Abulibdeh, A., Zaidan, E.S., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of Industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production, 437, 140527. https://doi.org/10.1016/j.jclepro.2023.140527 DOI: https://doi.org/10.1016/j.jclepro.2023.140527
Leal Filho, W., Ribeiro, P.C. C., Mazutti, J., Lange Salvia, A., Bonato Marcolin, C., Lima Silva Borsatto, J.M., … Viera Trevisan, L. (2024). Using artificial intelligence to implement the UN sustainable development goals at higher education institutions. International Journal of Sustainable Development & World Ecology, 31(6), 726-745. https://doi.org/10.1080/13504509.2024.2327584 DOI: https://doi.org/10.1080/13504509.2024.2327584
Ba, Y., & Liu, Z. (2022). Design and research of physical education platform based on artificial intelligence. Scientific Programming, 2022, 9327131. https://doi.org/10.1155/2022/9327131 DOI: https://doi.org/10.1155/2022/9327131
Cheng, D., Wang, H., & Li, M. (2022). Construction of sports training management information system using AI action recognition. Scientific Programming, 2022, 8393612. https://doi.org/10.1155/2022/8393612 DOI: https://doi.org/10.1155/2022/8393612
Wang, H., Jin, X., Zhang, T., & Wang, J. (2023). Convolutional neural network-based recognition method for volleyball movements. Heliyon, 9(8), e18124. https://doi.org/10.1016/j.heliyon.2023.e18124 DOI: https://doi.org/10.1016/j.heliyon.2023.e18124
Yu, H., & Mi, Y. (2023). Application model for innovative sports practice teaching in colleges using Internet of Things and artificial intelligence. Electronics, 12(4), 874. https://doi.org/10.3390/electronics12040874 DOI: https://doi.org/10.3390/electronics12040874
Kong, J. (2024). Design and optimization of physical education assessment index system based on big data analysis. IEEE Access, 12, 73238-73246. https://doi.org/10.1109/ACCESS.2024.3403139 DOI: https://doi.org/10.1109/ACCESS.2024.3403139
Peng, J., & Xu, J. (2022). Deep learning analysis on the resulting impacts of weekly load training on students’ biological system. Revista Brasileira de Medicina do Esporte, 29(spe1), e2022_0197. https://doi.org/10.1590/1517-8692202329012022_0197 DOI: https://doi.org/10.1590/1517-8692202329012022_0197
Chang, D.H., Lin, M.P. C., Hajian, S., & Wang, Q.Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), 12921. https://doi.org/10.3390/su151712921 DOI: https://doi.org/10.3390/su151712921
Filius, R.M., de Kleijn, R.A., Uijl, S.G., Prins, F.J., van Rijen, H.V., & Grobbee, D.E. (2018). Strengthening dialogic peer feedback aiming for deep learning in SPOCs. Computers & Education, 125, 86-100. https://doi.org/10.1016/j.compedu.2018.06.004 DOI: https://doi.org/10.1016/j.compedu.2018.06.004
Maphosa, V., & Maphosa, M. (2023). Artificial intelligence in higher education: A bibliometric analysis and topic modeling approach. Applied Artificial Intelligence, 37(1), 2261730. https://doi.org/10.1080/08839514.2023.2261730 DOI: https://doi.org/10.1080/08839514.2023.2261730
Yang, R., Yuan, Q., Zhang, W., Cai, H., & Wu, Y. (2024). Application of artificial intelligence in rehabilitation science: A scientometric investigation utilizing CiteSpace. SLAS Technology, 29(4), 100162. https://doi.org/10.1016/j.slast.2024.100162 DOI: https://doi.org/10.1016/j.slast.2024.100162
Pham, N., Ngoc, H.P., & Nguyen-Duc, A. (2025). Fairness for machine learning software in education: A systematic mapping study. Journal of Systems and Software, 219, 112244. https://doi.org/10.1016/j.jss.2024.112244 DOI: https://doi.org/10.1016/j.jss.2024.112244
van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3 DOI: https://doi.org/10.1007/s11192-009-0146-3
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317 DOI: https://doi.org/10.1002/asi.20317
Zawacki-Richter, O., Marín, V.I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510 DOI: https://doi.org/10.1109/ACCESS.2020.2988510
Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104 DOI: https://doi.org/10.1177/002224378101800104
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8 DOI: https://doi.org/10.1007/s11747-014-0403-8
Popenici, S.A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8 DOI: https://doi.org/10.1186/s41039-017-0062-8
Biggs, J., Kember, D., & Leung, D.Y. P. (2001). The revised two-factor Study Process Questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71(1), 133-149. https://doi.org/10.1348/000709901158433 DOI: https://doi.org/10.1348/000709901158433
Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., … Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102642
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 DOI: https://doi.org/10.1016/j.lindif.2023.102274
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7 DOI: https://doi.org/10.1007/s43681-021-00096-7
Chan, C.K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8 DOI: https://doi.org/10.1186/s41239-023-00411-8
Strzelecki, A. (2024). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 32(9), 5142-5155. https://doi.org/10.1080/10494820.2023.2209881 DOI: https://doi.org/10.1080/10494820.2023.2209881
Cotton, D.R., Cotton, P.A., & Shipway, J.R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148 DOI: https://doi.org/10.1080/14703297.2023.2190148
Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x DOI: https://doi.org/10.1186/s40561-023-00237-x
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html DOI: https://doi.org/10.1109/CVPR.2016.90
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539
Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84. https://doi.org/10.1016/j.iheduc.2015.10.002 DOI: https://doi.org/10.1016/j.iheduc.2015.10.002
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T DOI: https://doi.org/10.1016/0749-5978(91)90020-T
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K-12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9, 110558-110572. https://doi.org/10.1109/ACCESS.2021.3097962 DOI: https://doi.org/10.1109/ACCESS.2021.3097962
Demartini, C.G., Sciascia, L., Bosso, A., & Manuri, F. (2024). Artificial intelligence bringing improvements to adaptive learning in education: A case study. Sustainability, 16(3), 1347. https://doi.org/10.3390/su16031347 DOI: https://doi.org/10.3390/su16031347
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Mengshi Li, Binbin Zhang, Gaopeng Li, Weiming Tao, Mohd Taib Harun, Jian Li, Jinglang Fu
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
