Machine Learning in Physical Education, Sports, and Recreation: Opportunities, Challenges, and Ethical Considerations – A Systematic Review
DOI:
https://doi.org/10.56294/saludcyt20252251Keywords:
Machine learning, Physical Education, Sports, Recreation, Opportunities, Ethical considerationsAbstract
Introduction: Machine learning, a subfield of artificial intelligence, is rapidly transforming the landscape of sports science. It enables more informed decision-making across various sectors, including healthcare, agriculture, and, notably, sports. This study aims to investigate how machine learning can improve athletic performance, injury prevention, and coaching efficacy. Methods: This systematic review utilized a comprehensive search strategy across 510 articles to identify studies focused on machine learning (ML) in the fields of physical education, sports, and recreation. A total of 36 studies met the inclusion criteria and were thoroughly reviewed for their relevance to the outcomes within the search scope. The search commenced in January 2025 and continued through July 2025. It covered several databases, including PubMed, Scopus, Web of Science, ScienceDirect, and the Cochrane Library. The focus was on publications from 2015 to 2025, using keywords such as “artificial intelligence,” “athletic performance,” “coaching efficacy,” “injury prevention,” and “machine learning.”
Results: The results demonstrate that machine learning significantly enhances athletic performance, injury prevention, and coaching effectiveness. It facilitates tailored training and data-driven decision-making, which lead to improved skill development and rehabilitation outcomes. In the realm of physical education, machine learning supports personalized instruction that increases student engagement. However, challenges remain, including issues with data integrity, high computing costs, and a shortage of expertise. Ethical concerns—particularly related to privacy, bias, and transparency—require immediate attention. While machine learning has the potential to transform both sports and education, it must be implemented appropriately to ensure fairness, accuracy, and accessibility for all users.
Conclusions: The outcome indicates that machine learning enhances physical education and athletics by improving performance analysis, reducing injury risk, and enabling coaches to personalize training. Although there are challenges such as data quality and ethical concerns, the effective use of machine learning can significantly support athlete development. The combination of machine learning with coaching and educational methods fosters inclusive, data-driven strategies that improve skill acquisition, ensure safety, and promote the long-term well-being of athletes.
References
1. Ahamed S, Raghavendra SP. Fundamentals of Artificial Intelligence & Machine Learning. Academic Guru Publishing House; 2023 Oct 13.
2. Broussard M. Artificial unintelligence: How computers misunderstand the world. mit Press; 2018 Apr 27.
3. Claudino JG, Capanema DD, de Souza TV, Serrão JC, Machado Pereira AC, Nassis GP. Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review. Sports medicine-open. 2019 Dec;5(1):28.
4. Souaifi M, Dhahbi W, Jebabli N, Ceylan Hİ, Boujabli M, Muntean RI, Dergaa I. Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention. Bioengineering. 2025 Aug 20;12(8):887.
5. Van Eetvelde H, Mendonça LD, Ley C, Seil R, Tischer T. Machine learning methods in sport injury prediction and prevention: a systematic review. Journal of experimental orthopaedics. 2021 Apr 14;8(1):27.
6. Taylor R, Fakhimi M, Ioannou A, Spanaki K. Personalized learning in education: a machine learning and simulation approach. Benchmarking: An international journal. 2024 Aug 23.
7. Sampaio T, Oliveira JP, Marinho DA, Neiva HP, Morais JE. Applications of machine learning to optimize tennis performance: a systematic review. Applied Sciences. 2024 Jun 25;14(13):5517.
8. Jun W, Iqbal MS, Abbasi R, Omar M, Huiqin C. 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). 2024 Jan 1;20(1):1-6.https://doi.org/10.4018/ijswis.337961
9. Musat CL, Mereuta C, Nechita A, Tutunaru D, Voipan AE, Voipan D, Mereuta E, Gurau TV, Gurău G, Nechita LC. Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods. Diagnostics (Basel). 2024 Nov 10;14(22):2516. doi: 10.3390/diagnostics14222516
10. Liu S, Wu C, Xiao S, Liu Y, Song Y. Optimizing young tennis players' development: Exploring the impact of emerging technologies on training effectiveness and technical skills acquisition. PLoS One. 2024 Aug 7;19(8):e0307882. doi: 10.1371/journal.pone.0307882
11. Sigurdson H, Chan JH. Machine Learning Applications to Sports Injury: A Review. icSPORTS. 2021 Oct;2021:157-68.. https://doi.org/10.5220/0010717100003059
12. Dey V. The Role of Artificial Intelligence in Physical Education and Sports: A Review of Current Applications and Future Potential. Prof.(Dr.) Anju Singh Prof. Bharti Dixit. 2023 Dec:9. https://doi.org/10.31995/jgv.2023.v14is3.002
13. Taylor R, Fakhimi M, Ioannou A, Spanaki K. Personalized learning in education: a machine learning and simulation approach. Benchmarking: An International Journal. 2024 Aug 23.
14. Bodemer O. Enhancing individual sports training through artificial intelligence: A comprehensive review. Authorea Preprints. 2023 Oct 31.
15. Wan L. Design and adjustment of optimizing athletes' training programs using machine learning algorithms. J. Electrical Systems. 2024;20(6s):2014-24.
16. Mateus N, Abade E, Coutinho D, Gómez MÁ, Peñas CL, Sampaio J. Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management. Sensors (Basel). 2024 Dec 29;25(1):139. doi: 10.3390/s25010139
17. Khan MA, Habib M, Saqib S, Alyas T, Khan KM, Al Ghamdi MA, Almotiri SH. Analysis of the Smart Player's Impact on the Success of a Team Empowered with Machine Learning. Computers, Materials & Continua. 2021 Jan 1;66(1).
18. Sree TM, Sasikumar P, Ayesha S, Basha MS, Sucharitha MM. Predicting Player Engagement in Online Gaming: A Machine Learning Approach. In2024 IEEE North Karnataka Subsection Flagship International Conference (NKCon) 2024 Sep 21 (pp. 1-6). IEEE.
19. Aditya RS, Yunus M. Talent scouting and standardizing fitness data in football club: systematic review. Retos: nuevas tendencias en educación física, deporte y recreación. 2024(60):1382-9.
20. Tariq MU, Sergio RP. Innovative Assessment Techniques in Physical Education: Exploring Technology-Enhanced and Student-Centered Models for Holistic Student Development. InGlobal Innovations in Physical Education and Health 2025 (pp. 85-112). IGI Global.
21. Fadare SA, Gulanes AA, De la Cruz Torres J, Guiao EM, Tagaylo JP. Enhancing physical activity through information technology: Current trends and future directions. Salud, Ciencia y Tecnología. 2024 May 9;4:950-.
22. Zou R. Exploring the Role of Artificial Intelligence in Sports Injury Prevention and Rehabilitation. Scalable Computing: Practice and Experience. 2025 Jan 5;26(1):316-25.
23. Iduh BN, Umeh MN, Anusiuba OI, Egba FA. Development of a Predictive Modeling Framework for Athlete Injury Risk Assessment and Prevention: A Machine Learning Approach. European Journal of Theoretical and Applied Sciences. 2024;2(4):894-906.
24. Gulanes AA, Fadare SA, Pepania JE, Hanima CO. Preventing Sports Injuries: A Review of Evidence-Based Strategies and Interventions. Salud, Ciencia y Tecnología. 2024(4):951.
25. Ayala RE, Granados DP, Gutiérrez CA, Ruíz MA, Espinosa NR, Heredia EC. Novel study for the early identification of injury risks in athletes using machine learning techniques. Applied Sciences. 2024 Jan 9;14(2):570.
26. Schulc A, Leite CBG, Csákvári M, Lattermann L, Zgoda MF, Farina EM, Lattermann C, Tősér Z, Merkely G. Identifying Anterior Cruciate Ligament Injuries Through Automated Video Analysis
of In-Game Motion Patterns. Orthop J Sports Med. 2024 Mar 12;12(3):23259671231221579. doi: 10.1177/23259671231221579
27. Kaswan KS, Dhatterwal JS, Ojha RP. AI in personalized learning. InAdvances in technological innovations in higher education 2024 Mar 29 (pp. 103-117). CRC Press.
28. Fadare AS, Cruz TJ, Rodriguez RM, Sacopla KN, Chavez EM. The Unmasked Coach: Mastering Decision-Making Skills. Tuijin Jishu/Journal of Propulsion Technology4 (4); 4358. 2023;4366.
29. Pashaie S, Mohammadi S, Golmohammadi H. Unlocking athlete potential: The evolution of coaching strategies through artificial intelligence. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology. 2024:17543371241300889.
30. Mittal S, Mohapatra SS, Kanwal P, Ta HH. Game Tactics and Strategy Optimization Through AI and Machine Learning. InAI and Machine Learning Applications in Sports Analytics 2025 (pp. 77-102). IGI Global Scientific Publishing.
31. Borrohou S, Fissoune R, Badir H. Data cleaning survey and challenges–improving outlier detection algorithm in machine learning. Journal of Smart Cities and Society. 2023 Oct 9;2(3):125-40.
32. Whang SE, Lee JG. Data collection and quality challenges for deep learning. Proceedings of the VLDB Endowment. 2020 Aug 1;13(12):3429-32.
33. Fonti F, Ross JM, Aversa P. Using sports data to advance management research: A review and a guide for future studies. Journal of Management. 2023 Jan;49(1):325-62.
34. Davis J, Bransen L, Devos L, Jaspers A, Meert W, Robberechts P, Van Haaren J, Van Roy M. Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned. Machine Learning. 2024 Sep;113(9):6977-7010.
35. Nandan Prasad A. Data Quality and Preprocessing. InIntroduction to Data Governance for Machine Learning Systems: Fundamental Principles, Critical Practices, and Future Trends 2024 Dec 14 (pp. 109-223). Berkeley, CA: Apress.
36. Abhirami K, Devi MK. Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences. Computer Systems Science & Engineering. 2022 Mar 1;40(3).
37. Rusyn B, Lutsyk O, Kosarevych R, Obukh Y. Application peculiarities of deep learning methods in the problem of big datasets classification. InFuture Intent-Based Networking: On the QoS Robust and Energy Efficient Heterogeneous Software Defined Networks 2021 Dec 10 (pp. 493- 506). Cham: Springer International Publishing.
38. Rossi JG, Rojas-Perilla N, Krois J, Schwendicke F. Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy. JAMA Network Open. 2022 Mar 1;5(3):e220269-.
39. Islam R, Patamsetti V, Gadhi A, Gondu RM, Bandaru CM, Kesani SC, Abiona O. The future of cloud computing: benefits and challenges. International Journal of Communications, Network and System Sciences. 2023 Apr 17;16(4):53-65.
40. Carneiro D, Veloso P. Ethics, transparency, fairness and the responsibility of artificial intelligence. InNew Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence: The DITTET Collection 1 2022 (pp. 109-120). Springer International Publishing.
41. Akinrinola O, Okoye CC, Ofodile OC, Ugochukwu CE. Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability. GSC Advanced Research and Reviews. 2024;18(3):050-8.
42. Kusan M, Arman AR. Ethical and Security Issues: The Impact of Artificial Intelligence in Sports Management. Acta Scientiae et Intellectus. 2024;10(2):78-.
43. Weinberg FJ, Scandura T. Advancing the future of workplace development: integrative approaches to mentoring and coaching. Journal of Managerial Psychology. 2024 Aug 21;39(6):832-43.
44. Reis FJJ, Alaiti RK, Vallio CS, Hespanhol L. Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives. Braz J Phys Ther. 2024 May-Jun;28(3):101083. doi: 10.1016/j.bjpt.2024.101083
45. Chen Z, Song X, Zhang Y, Wei B, Liu Y, Zhao Y, Wang K, Shu S. Intelligent Recognition of Physical Education Teachers' Behaviors Using Kinect Sensors and Machine Learning. Sensors & Materials. 2022 Mar 25;34.
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Copyright (c) 2025 Stephen Ayoade Fadare, Jay-R Beterbo, Frederick Ybanez, Jack Ryan Abdulrajak Isahac, Maria Cecilia Fadare, Edris Aloy Barahimin, Sittie Aina T. Pandapatan (Author)
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