Assessing the Efficacy of an Artificial Intelligence-Driven Real-Time Exercise Monitoring System in Computer-Supported Collaborative Learning
DOI:
https://doi.org/10.47197/retos.v67.114434Keywords:
real-time monitoring, physical education, learning engagement, motivation, injury preventionAbstract
Introduction: the efficacy of AI-driven real-time exercise monitoring systems in enhancing physical education programs was explored in this study. the integration of such technologies aimed to boost engagement, motivation, and safety among participants, reflecting a growing trend towards technology-enhanced learning environments.
Objective: the objective was to empirically evaluate the impact of an AI-driven system on learning engagement, motivation levels, and injury prevention in a physical education context, comparing outcomes against traditional training methods.
Methodology: the methodology involved a controlled experiment with eighty physical education students divided into control and experimental groups. data were collected through surveys, performance assessments, and injury reports, with statistical analyses conducted using independent samples t-tests and chi-square tests.
Results: results indicated significantly higher engagement and motivation in the experimental group, which utilized the AI system. additionally, this group experienced fewer injuries, demonstrating the system’s potential to enhance safety.
Discussion: other studies have similarly highlighted technology’s role in improving educational outcomes, though few have focused specifically on physical education. this study’s findings align with broader research supporting the adoption of AI in educational settings.
Conclusions: the conclusions confirm that AI-driven monitoring systems significantly improve student engagement, motivation, and safety in physical education, suggesting that such technologies can be valuable additions to educational curricula to enhance learning experiences and outcomes.
References
Kaliisa, R., Lopez-Pernas, S., Misiejuk, K., Damsa, C., Sobocinski, M., Jarvela, S., & Saqr, M. (2025). A Topical Review of Research in Computer-Supported Collaborative Learning: Questions and Possibilities. Computers & Education, 105246. https://doi.org/10.1016/j.compedu.2025.105246
Kang, J., Xu, X., & Yan, L. (2024). Leveraging affordances of immersive technology-supported collaborative learning (ITCL): A systematic review. Education and Information Technologies, 1-41. https://doi.org/10.1007/s10639-024-13079-y
Deng, C., Feng, L., & Ye, Q. (2024). Smart physical education: Governance of school physical education in the era of new generation of information technology and knowledge. Journal of the Knowledge Economy, 15(3), 13857-13889. https://doi.org/10.1007/s13132-023-01668-0
Liu, Y., Sathishkumar, V. E., & Manickam, A. (2022). Augmented reality technology based on school physical education training. Computers and Electrical Engineering, 99, 107807. https://doi.org/10.1016/j.compeleceng.2022.107807
Cao, F., Xiang, M., Chen, K., & Lei, M. (2022). Intelligent physical education teaching tracking system based on multimedia data analysis and artificial intelligence. Mobile Information Systems, 2022(1), 7666615. https://doi.org/10.1155/2022/7666615
Xie, M. (2021). Design of a physical education training system based on an intelligent vision. Computer Applications in Engineering Education, 29(3), 590-602. https://doi.org/10.1002/cae.22259
Wang, C., & Du, C. (2022). Optimization of physical education and training system based on machine learning and Internet of Things. Neural Computing and Applications, 1-16. https://doi.org/10.1007/s00521-021-06278-y
Hannan, A., Shafiq, M. Z., Hussain, F., & Pires, I. M. (2021). A portable smart fitness suite for real-time exercise monitoring and posture correction. Sensors, 21(19), 6692. https://doi.org/10.3390/s21196692
Ren, G., Huang, Z., You, S., Lin, W., Huang, T., Wang, G., & Lee, J. H. (2025). Enhancing Motor Skills and Coordination with Visual-Haptic Feedback in Ball Sport Training. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3547159
Zhang, Z., Chen, L., Qin, Z., He, J., Gao, C., Sun, J., ... & Li, D. (2024). Effects of functional correction training on movement patterns and physical fitness in male college students. PeerJ, 12, e16878. https://doi.org/10.7717/peerj.16878
Kovoor, M., Durairaj, M., Karyakarte, M. S., Hussain, M. Z., Ashraf, M., & Maguluri, L. P. (2024). Sensor-enhanced wearables and automated analytics for injury prevention in sports. Measurement: Sensors, 32, 101054. https://doi.org/10.1016/j.measen.2024.101054
Hsia, L. H., Hwang, G. J., & Hwang, J. P. (2024). AI-facilitated reflective practice in physical education: An auto-assessment and feedback approach. Interactive Learning Environments, 32(9), 5267-5286. https://doi.org/10.1080/10494820.2023.2212712
Wang, J., Yang, Y., Liu, H., & Jiang, L. (2024). Enhancing the college and university physical education teaching and learning experience using virtual reality and particle swarm optimization. Soft Computing, 28(2), 1277-1294. https://doi.org/10.1007/s00500-023-09528-4
Kadhim, M. A. A., Mashi, A. A. A., Al-Diwan, L. H., & Ghazi, M. A. (2024). Understanding the Mechanism of Conducting Benchmark Test for the Infrastructure of Physical Education Curricula in the Age of Artificial Intelligence. International Journal of Elementary Education, 13(1), 8-12. https://doi.org/10.11648/j.ijeedu.20241301.12
Trendowski, T. (2025). A Checklist for Using Generative Artificial Intelligence to Create Lesson Plans in K–12 Physical Education. Journal of Physical Education, Recreation & Dance, 96(2), 15-27. https://doi.org/10.1080/07303084.2024.2437988.
Cui, G. (2025). Empowering High-Quality Development of Sports Qualities and Physical Health of Primary and Secondary School Students in the Region Through Digitalization. In Digital Transformation of Regional Education in China (pp. 179-186). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-8144-7_25
Arif, Y. M., Nugroho, F., Aini, Q., Fauzan, A. C., & Garcia, M. B. (2025). A Systematic Literature Review of Serious Games for Physical Education: Technologies, Implementations, and Evaluations. Global Innovations in Physical Education and Health, 1-36. https://doi.org/10.4018/979-8-3693-3952-7.ch001
Jun, W., Iqbal, M. S., Abbasi, R., Omar, M., & Huiqin, C. (2024). Web-semantic-driven machine learning and blockchain for transformative change in the future of physical education. International Journal on Semantic Web and Information Systems (IJSWIS), 20(1), 1-16. https://doi.org/10.4018/IJSWIS.337961
Kotte, H., Daiber, F., Kravcik, M., & Duong-Trung, N. (2024, June). Fitsight: Tracking and feedback engine for personalized fitness training. In Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 223-231). https://doi.org/10.1145/3627043.3659547
Mitra, U., & Rehman, S. U. (2025). Significance of AI/ML Wearable Technologies for Education and Teaching. In Wearable Devices and Smart Technology for Educational Teaching Assistance (pp. 1-26). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7817-5.ch001
Mateus, N., Abade, E., Coutinho, D., Gómez, M. Á., Peñas, C. L., & Sampaio, J. (2024). Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management. Sensors, 25(1), 139. https://doi.org/10.3390/s25010139
Li, X., Fan, D., Deng, Y., Lei, Y., & Omalley, O. (2024). Sensor fusion-based virtual reality for enhanced physical training. Robotic Intelligence and Automation, 44(1), 48-67. https://doi.org/10.1108/RIA-08-2023-0103
Coskun, H. (2025). A Contactless Real-Time System to Classify Multi-Class Sitting Posture Using Depth Sensor-Based Data. In AI-Driven Innovation in Healthcare Data Analytics (pp. 333-368). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7277-7.ch011
Voltmer, J. B., Froehlich, L., Reich-Stiebert, N., Raimann, J., & Stürmer, S. (2024). Group cohesion and performance in Computer-Supported Collaborative Learning (CSCL): Using assessment analytics to understand the effects of multi-attributional diversity. In Assessment Analytics in Education: Designs, Methods and Solutions (pp. 113-132). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-56365-2_6
Silseth, K., Steier, R., & Arnseth, H. C. (2024). Exploring students’ immersive VR experiences as resources for collaborative meaning making and learning. International Journal of Computer-Supported Collaborative Learning, 19(1), 11-36. https://doi.org/10.1007/s11412-023-09413-0
Yuan, J., Qin, L., Li, Z., Jiang, B., Diao, Y., & Liu, G. (2024, November). From Unilateral Dominance to Collaborative Cooperation: Exploring Intergenerational Cooperative Exergames to Facilitate Physical Exercise for Older Adults. In Companion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing (pp. 369-375). https://doi.org/10.1145/3678884.3681877
An, P. (2024). Fuzzy Decision Support Systems to Improve the Effectiveness of Training Programs in the Field of Sports Fitness. International Journal of Computational Intelligence Systems, 17(1), 168. https://doi.org/10.1007/s44196-024-00555-z
Ma, S. (2024). Integrating sports education with data analysis and computer technology: A new paradigm for enhanced athletic performance. Applied and Computational Engineering, 57, 178-183. https://doi.org/10.54254/2755-2721/57/20241330
Ramadhan, R., Effendy, F., & Pratama, A. P. (2024). Sports Education on Student Learning Motivation Seen from the Roles Involved in Sport Education Using Handball. Indonesian Journal of Physical Education and Sport Science, 4(1), 22-30.
Ferraz, R., Ribeiro, D., Alves, A. R., Teixeira, J. E., Forte, P., & Branquinho, L. (2024). Using Gamification in Teaching Physical Education: A survey review. Montenegrin Journal of Sports Science & Medicine, 13(1). https://doi.org/10.26773/mjssm.240304
Safitri, R., Wahyuri, A. S., & Ockta, Y. (2024). The Impacts of the Project-Based Learning and Problem-Based Learning Models with Self-Confidence on Students’ Learning Outcomes. Indonesian Research Journal in Education| IRJE|, 8(1), 269-283. https://doi.org/10.22437/irje.v8i1.31480
Lobo, M. D., Tavares, S. M., de Almeida, R. P. P., & Garcia, M. B. (2025). Advancing Precision in Physical Education and Sports Science: A Review of Medical Imaging Methods for Assessing Body Composition. Global Innovations in Physical Education and Health, 293-326. https://doi.org/10.4018/979-8-3693-3952-7.ch011
Song, X. (2024). Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network. Scientific Reports, 14(1), 4104. https://doi.org/10.1038/s41598-024-53964-7
Qi, Y., Sajadi, S. M., Baghaei, S., Rezaei, R., & Li, W. (2024). Digital technologies in sports: Opportunities, challenges, and strategies for safeguarding athlete wellbeing and competitive integrity in the digital era. Technology in Society, 102496. https://doi.org/10.1016/j.techsoc.2024.102496
Pilkington, V., Rice, S., Olive, L., Walton, C., & Purcell, R. (2024). Athlete mental health and wellbeing during the transition into elite sport: strategies to prepare the system. Sports medicine-open, 10(1), 24. https://doi.org/10.1186/s40798-024-00690-z
Streetman, A. E., & Heinrich, K. M. (2024). Female empowerment through sport: an exploratory narrative review. Sport in Society, 27(5), 804-819. https://doi.org/10.1080/17430437.2023.2270443
Yılmaz, O., Soylu, Y., Erkmen, N., Kaplan, T., & Batalik, L. (2024). Effects of proprioceptive training on sports performance: a systematic review. BMC Sports Science, Medicine and Rehabilitation, 16(1), 149. https://doi.org/10.1186/s13102-024-00936-z
Breed, R., Lindsay, R., Kittel, A., & Spittle, M. (2024). Content and quality of comparative tactical game-centered approaches in physical education: A systematic review. Review of Educational Research, 00346543241227236. https://doi.org/10.3102/00346543241227236
Washif, J., Pagaduan, J., James, C., Dergaa, I., & Beaven, C. (2024). Artificial intelligence in sport: Exploring the potential of using ChatGPT in resistance training prescription. Biology of sport, 41(2), 209-220. https://doi.org/10.5114/biolsport.2024.132987
Singh, G., George, R. P., Ahmad, N., Hussain, S., Ather, D., & Kler, R. (2024). A deep learning approach for evaluating the efficacy and accuracy of PoseNet for posture detection. International Journal of System Assurance Engineering and Management, 1-10. https://doi.org/10.1007/s13198-024-02530-5
Mishra, S., Bhardwaj, I., Singh, A., Chakraborty, T., & Tripathi, A. (2025). Real-time pose correction and wellness tracking system for enhanced Yoga practice. In Challenges in Information, Communication and Computing Technology (pp. 383-388). CRC Press.
Yadav, S. (2025). Leveraging AI to Enhance Teaching and Learning in Education: The Role of Artificial Intelligence in Modernizing Classroom Practices. In Optimizing Research Techniques and Learning Strategies With Digital Technologies (pp. 211-238). IGI Global Scientific Publishing.
Huang, M., & Yongquan, T. (2025). Tech‐driven excellence: A quantitative analysis of cutting‐edge technology impact on professional sports training. Journal of Computer Assisted Learning, 41(1), e13082. https://doi.org/10.1111/jcal.13082
Li, Z., Wang, L., & Wu, X. (2025). Artificial intelligence based virtual gaming experience for sports training and simulation of human motion trajectory capture. Entertainment Computing, 52, 100828. https://doi.org/10.1016/j.entcom.2024.100828
Geisen, M., & Klatt, S. (2022). Real-time feedback using extended reality: A current overview and further integration into sports. International Journal of Sports Science & Coaching, 17(5), 1178-1194. https://doi.org/10.1177/17479541211051006
Manninen, M., & Campbell, S. (2022). The effect of the Sport Education Model on basic needs, intrinsic motivation and prosocial attitudes: A systematic review and multilevel meta-analysis. European Physical Education Review, 28(1), 78-99. https://doi.org/10.1177/1356336X211017938
Fidan, M., & Gencel, N. (2022). Supporting the instructional videos with chatbot and peer feedback mechanisms in online learning: The effects on learning performance and intrinsic motivation. Journal of Educational Computing Research, 60(7), 1716-1741. https://doi.org/10.1177/07356331221077901
Schüler, J., Wolff, W., & Duda, J. L. (2023). Intrinsic motivation in the context of sports. In Sport and exercise psychology: theory and application (pp. 171-192). Cham: Springer International Publishing.
Yin, J., Goh, T. T., & Hu, Y. (2024). Using a chatbot to provide formative feedback: A longitudinal study of intrinsic motivation, cognitive load, and learning performance. IEEE Transactions on Learning Technologies, 17, 1378-1389.
Amaro, N., Monteiro, D., Rodrigues, F., Matos, R., Jacinto, M., Cavaco, B., ... & Antunes, R. (2023). Task-involving motivational climate and enjoyment in youth male football athletes: The mediation role of self-determined motivation. International Journal of Environmental Research and Public Health, 20(4), 3044. https://doi.org/10.3390/ijerph20043044
Uhm, J. P., Kim, S., & Lee, H. W. (2023). Stimulating suspense in gamified virtual reality sports: Effect on flow, fun, and behavioral intention. International Journal of Human–Computer Interaction, 39(19), 3846-3858. https://doi.org/10.1080/10447318.2022.2107782
Nur, L., Hong, F., Al Ardha, M. A., Burhaein, E., & Malik, A. A. (2023). Direct instruction with task sheet-based learning model: an alternative approach to encourage learning motivation during the Covid-19 crisis. International Journal of Instruction, 16(3), 843-854. https://e-iji.net/ats/index.php/pub/article/view/110
Tang, Y., Zhang, X., & Zan, S. (2024). Exploring e-sports fans’ motivation for watching live streams based on self-determination theory. Scientific Reports, 14(1), 13858. https://doi.org/10.1038/s41598-024-64712-2
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nurzhamal Oshanova, Guldina Bolshevikovna, Nazym Tekesbayeva, Shugyla Turashova, Gulzat Anuarbekova, Assel Bukanova
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and ensure the magazine the right to be the first publication of the work as licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of authorship of the work and the initial publication in this magazine.
- Authors can establish separate additional agreements for non-exclusive distribution of the version of the work published in the journal (eg, to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Is allowed and authors are encouraged to disseminate their work electronically (eg, in institutional repositories or on their own website) prior to and during the submission process, as it can lead to productive exchanges, as well as to a subpoena more Early and more of published work (See The Effect of Open Access) (in English).
This journal provides immediate open access to its content (BOAI, http://legacy.earlham.edu/~peters/fos/boaifaq.htm#openaccess) on the principle that making research freely available to the public supports a greater global exchange of knowledge. The authors may download the papers from the journal website, or will be provided with the PDF version of the article via e-mail.
