Yan Gao- Capital University of Physical Education and Sports, Institute of Artificial Intelligence in Sports, Beijing, China
With the rapid development of artificial intelligence (AI) technology, particularly in the field of physical education in higher education institutions, the application of AI has shown significant potential. AI not only offers innovative teaching models and evaluation systems for physical education, but also enhances teaching efficiency, enables personalized instruction, and improves students' athletic performance. In the context of public health, AI's role becomes even more crucial, as it assists in developing scientific exercise plans through precise motion data analysis, thereby promoting both physical and mental health. Furthermore, AI technology can drive innovation in the content and methods of public physical education teaching, providing robust support for high-quality sports education. Studies indicate that AI has optimized the physical education process, spurred the innovation of curriculum content, and facilitated the transformation of teaching models, injecting new momentum into the sustainable development of physical education in universities and the achievement of public health goals.
1 Introduction
With the rapid advancement of artificial intelligence (AI) technology, its application in higher education physical education (PE) has transitioned from experimentation to practical implementation. Research suggests that AI-driven reforms are transforming traditional PE methods into intelligent, data-driven instructional models (1). By integrating real-time data analysis, AI enables personalized teaching adjustments based on students' learning progress and athletic abilities, significantly enhancing engagement and performance. A survey of 200 students revealed that AI-assisted teaching increased student motivation by ~30% (1). Beyond skill improvement, this approach also contributes to long-term physical health and helps mitigate public health risks such as obesity (2).
AI-based motion analysis further enhances teaching quality by enabling precise, data-driven instructional strategies. Motion recognition systems track students' movements in real-time, providing accurate feedback and refining technique execution. Consequently, AI-driven intelligent curriculum reform has gained widespread adoption, making PE instruction more personalized and adaptable (3, 4). For instance, an AI-integrated PE reform experiment showed that 80% of 150 participating students reported better mastery of athletic skills through AI-assisted courses, leading to sustained interest in physical activity (3). Furthermore, AI has introduced advanced assessment methods that enhance accuracy and personalization. AI systems analyze students' athletic data to develop individualized learning paths, significantly improving performance, particularly in coordination and endurance (5–7). These systems continuously monitor movement patterns, offering real-time feedback and adjusting training accordingly, thereby optimizing skill development and facilitating early detection of potential health risks (8).
From a broader public health perspective, AI-driven interventions support early identification of health issues and personalized exercise plans, aligning PE with public health objectives (8). Additionally, AI necessitates upskilling among educators. A survey of 500 PE teachers indicated that 60% lacked adequate AI training, hindering effective technology integration. To address this, universities are introducing AI training programs to enhance teachers' proficiency in data analytics and AI-assisted instruction (8, 9).
Additionally, AI holds significant potential in preventing sports injuries by integrating physiological data (e.g., electromyography, heart rate variability) and employing machine learning to predict injury risks (10). AI-driven biomechanical modeling can identify movement inefficiencies, optimize training, and reduce fatigue-induced errors (11). Wearable AI systems provide real-time feedback, preventing overtraining and enhancing recovery strategies (10). Future AI-driven rehabilitation programs will further improve performance and injury prevention.
This paper explores the role of AI in higher education PE, with a focus on enhancing athletic performance, promoting health management, and achieving public health goals. The findings provide valuable insights for the future development of university sports programs.
2 AI's role in public physical education reform
AI is transforming higher education PE by introducing personalized instruction, intelligent assessment, and teacher development strategies. Unlike traditional “one-size-fits-all” methods, AI-driven systems create customized learning experiences based on individual physiological and performance data, thereby optimizing training intensity and minimizing injury risk (5–7).
AI enhances teaching effectiveness through real-time performance tracking and feedback. By leveraging data analytics, teachers can dynamically adjust lesson plans to align with students' progress, shifting from conventional “one-to-many” instruction to an interactive, student-centered approach (1, 12–14). Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) further boost engagement, making physical education more effective and enjoyable (15).
Furthermore, AI significantly improves injury prevention in sports training. By analyzing movement biomechanics and identifying inefficient motion patterns, AI can help athletes refine their techniques and reduce the likelihood of overuse injuries (16–18). Research indicates that AI-based motion recognition systems can predict injury risk with 85% accuracy by assessing an athlete's movement deviations and muscular imbalances (17). Wearable AI technologies further enhance real-time monitoring, allowing for immediate feedback and personalized adjustments in training loads to mitigate injury risks (19). AI-driven rehabilitation programs also accelerate post-injury recovery by customizing exercises based on real-time physiological responses, thereby improving rehabilitation outcomes and minimizing re-injury rates.
Additionally, AI facilitates professional development for educators. Since effective AI integration requires technical proficiency, teacher training programs are increasingly incorporating AI literacy modules (9, 10). AI-driven platforms also promote equitable access to quality PE resources, benefiting students in under-resourced areas (20, 21). By enabling data-informed decision-making, AI helps ensure that PE interventions effectively enhance student fitness and align with public health objectives (9, 10).
3 Pathways for AI to enhance higher education PE
To achieve high-quality development in PE, institutions must invest in AI-driven innovations, optimize resource allocation, and adopt technology-enhanced training methods. AI improves efficiency in resource utilization by analyzing student engagement metrics and optimizing facility usage, leading to a 20–30% increase in effectiveness (22, 23). Empirical data indicates that AI-driven optimizations significantly boost student participation and attendance in PE programs. For instance, a university implementing AI-assisted training reported a 25% rise in student participation and a 15% increase in class attendance (24–26). AI dynamically tailors course content based on real-time feedback, ensuring personalized and engaging learning experiences.
Beyond PE, AI also plays a crucial role in public health management. Large-scale data analytics enable policymakers to develop targeted health promotion strategies, addressing prevalent issues such as chronic disease and sedentary lifestyles (27–30). Moreover, AI-powered VR and AR applications provide inclusive exercise opportunities for individuals facing spatial or physical constraints, expanding access to fitness resources (31–34). AI also enhances teaching quality through real-time feedback mechanisms. By continuously tracking students' exercise metrics, AI helps educators refine instructional methods, thereby improving student outcomes (35). Additionally, AI supports health monitoring by identifying potential risks and providing personalized recommendations, reinforcing the role of PE in long-term wellbeing (13, 36).
AI's role in performance enhancement extends to personalized athletic training. AI-based systems analyze an athlete's biomechanics and physiological data to create customized training regimens that optimize performance while minimizing fatigue (37). Research has shown that AI-assisted training improves strength, endurance, and coordination by up to 20% compared to traditional training programs (38). Furthermore, AI-driven predictive analytics can identify potential performance plateaus, allowing for timely modifications in training plans to sustain continuous improvement (35).
In facility management, AI-driven smart sports venues optimize environmental conditions to enhance exercise experiences. Automated adjustments to temperature, humidity, and lighting contribute to improved performance and injury prevention (39, 40). Smart booking systems further streamline access to sports facilities, increasing student participation in physical activities (41).
Although artificial intelligence demonstrates significant potential in higher education physical education (PE), this study has certain limitations. Firstly, the accuracy and generalizability of current AI systems in motion analysis are still constrained; individual differences and diverse sporting contexts may lead to biases in data interpretation. Secondly, some findings rely on survey data and case studies, which may lack sufficient sample size and experimental control. Additionally, the varying levels of AI literacy among educators could impede the effective integration of AI technologies into teaching practices. Future research should focus on strengthening empirical validation, expanding participant samples, and enhancing the usability and adaptability of AI-assisted instructional tools (Table 1).
Table 1. Applications of artificial intelligence in higher education physical education (PE).
4 Conclusion
The integration of AI in higher education PE is revolutionizing instructional models, improving teaching quality, and advancing public health initiatives. AI-driven data analysis and intelligent assessments facilitate personalized training programs, optimizing student performance while preventing injuries. The adoption of immersive technologies such as VR and AR further enhances engagement and participation, breaking traditional barriers to sports education.
AI is also reshaping teacher training, resource allocation, and public health strategies, promoting equitable access to quality PE. As AI continues to evolve, its applications in sports education will expand, fostering smarter, more effective training methodologies and supporting national fitness initiatives. By leveraging AI's capabilities, universities can drive innovation in PE, ultimately contributing to a healthier society.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
YG: Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Beijing Youth Support Project 2024-2026 (the project code of the financial system of Capital University of Physical Education and Sports is 153224014).
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Gen AI was used in the creation of this manuscript.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1. Pham T, Nguyen TB, Ha S, Ngoc NT. Digital transformation in engineering education: exploring the potential of AI-assisted learning. Australas J Educ Technol. (2023) 39:1–19. doi: 10.14742/ajet.8825
2. Liu Q, Li Z, Liu S. Review on intelligent governance of physical fitness of college students under the background of big data. Discrete Dyn Nat Soc. (2022). doi: 10.1155/2022/4010882
3. Cao F, Lei M, Lin S, Xiang M. Application of artificial intelligence-based big data AI technology in physical education reform. Mob Inf Syst. (2022). doi: 10.1155/2022/4017151
4. Hsia LH, Hwang GJ, Hwang JP. AI-facilitated reflective practice in physical education: an auto-assessment and feedback approach. Interact Learn Environ. (2024) 32:5267–86. doi: 10.1080/10494820.2023.2212712
5. Wang X, Liu YG. Cooperative learning method in physical education teaching based on multiple intelligence theory. Educ Sci Theory Pract. (2018) 18:2176–86. doi: 10.12738/estp.2018.5.117
6. Wang JH, Wang ZB. Research on college physical education teaching mode based on multimedia teaching system. Agro Food Ind Hi Tech. (2017) 28:353–6. Available online at: https://webofscience.clarivate.cn/wos/woscc/full-record/WOS:000405992100090
7. Zhang N, Zhang Z. Research on teaching practice growth mode of students major in physical education. Eurasia J Math Sci Technol Educ. (2017) 13:7111–20. doi: 10.12973/ejmste/78737
8. Miller A, Eather N, Gray S, Sproule J, Williams C, Gore J, et al. Can continuing professional development utilizing a game-centred approach improve the quality of physical education teaching delivered by generalist primary school teachers? Eur Phys Educ Rev. (2017) 23:171–95. doi: 10.1177/1356336X16642716
9. Park J, Yun S, Choi E. Professional identity of physical education teachers: what are the key components? J Teach Phys Educ. (2024) 1–11. doi: 10.1123/jtpe.2024-0078
10. Pakosz P, Domaszewski P, Konieczny M, Baczkowicz D. Muscle activation time and free-throw effectiveness in basketball. Sci Rep. (2021) 11:7489. doi: 10.1038/s41598-021-87001-8
11. Nakano N, Inaba Y, Fukashiro S, Yoshioka S. Basketball players minimize the effect of motor noise by using near-minimum release speed in free-throw shooting. Hum Mov Sci. 70:102583. doi: 10.1016/j.humov.2020.102583
12. Hao MM, Wang Y, Peng J. Empirical research on AI technology-supported precision teaching in high school science subjects. Appl Sci-Basel. (2024) 14:7544. doi: 10.3390/app14177544
13. Guo JQ, Bai L, Yu Z, Zhao Z, Wan B. An AI-application-oriented in-class teaching evaluation model by using statistical modeling and ensemble learning. Sensors. (2021) 21:241. doi: 10.3390/s21010241
14. Ng DT, Lee M, Tan RJ, Hu X, Downie JS, Chu SK. A review of AI teaching and learning from 2000 to 2020. Educ Inf Technol. (2023) 28:8445–501. doi: 10.1007/s10639-022-11491-w
15. Pu YF, Yang YH. Application of virtual reality technology in martial arts situational teaching. Mob Inf Syst. (2022) 2022:6497310. doi: 10.1155/2022/6497310
16. Faigenbaum AD, Myer GD. Resistance training among young athletes: safety, efficacy and injury prevention effects. Br J Sports Med. (2010) 44:56–63. doi: 10.1136/bjsm.2009.068098
17. Musat CL, Mereuta C, Nechita A, Tutunaru D, Voipan AE, Voipan D, et al. Diagnostic applications of AI in sports: a comprehensive review of injury risk prediction methods. Diagnostics. (2024) 14:2516. doi: 10.3390/diagnostics14222516
18. Chen ZL, Dai XH. Utilizing AI and IoT technologies for identifying risk factors in sports. Heliyon. (2024) 10:e32477. doi: 10.1016/j.heliyon.2024.e32477
19. Li X, Cui ZY, Li YC, Zhang JS. Real-time injury risk assessment and early warning for soccer players utilizing sensors and machine learning. Revista Internacional De Medicina Y Ciencias De La Actividad Fisica Y Del Deporte. (2025) 25:501–15. doi: 10.15366/rimcafd2025.99.032
20. Shih WY, Pu YL, Ho TK. Implementation of a teaching model of postmodern physical education. S Afr J Res Sport Phys Educ Recreat. (2021) 43:99–110. Available online at: https://webofscience.clarivate.cn/wos/woscc/full-record/WOS:000704720900008
21. Xie Q, Jin N, Lu SS. Lightweight football motion recognition and intensity analysis using low-cost wearable sensors. Appl Bionics Biomech. (2023) 2023:2354728. doi: 10.1155/2023/2354728
22. Wang IL, Wang LI, Liu Y, Su Y, Yao S, Ho CS. Application of real-time visual feedback system in balance training of the center of pressure with smart wearable devices. Int J Environ Res Public Health. (2021) 18:9637. doi: 10.3390/ijerph18189637
23. Zhang YC, Duan W, Villanueva LE, Chen S. Transforming sports training through the integration of internet technology and artificial intelligence. Soft Comput. (2023) 27:15409–23. doi: 10.1007/s00500-023-08960-w
24. Kong C, Ping J, Zheng XC. Application research of artificial intelligence technology in physical education: based on ecological theory. Fresenius Environ Bull. (2021) 30:266–71. Available online at: https://webofscience.clarivate.cn/wos/woscc/full-record/WOS:000629181200030
25. Zhang JY. Reform and innovation of artificial intelligence technology for information service in university physical education. J Intell Fuzzy Syst. (2021) 40:3325–35. doi: 10.3233/JIFS-189372
26. Yu HT, Mi Y. Application model for innovative sports practice teaching in colleges using internet of things and artificial intelligence. Electronics. (2023) 12:874. doi: 10.3390/electronics12040874
27. Hills AP, Dengel DR, Lubans DR. Supporting public health priorities: recommendations for physical education and physical activity promotion in schools. Prog Cardiovasc Dis. (2015) 57:368–74. doi: 10.1016/j.pcad.2014.09.010
28. Brissette I, Wales K, O'Connell M. Evaluating the wellness school assessment tool for use in public health practice to improve school nutrition and physical education policies in New York. J Sch Health. (2013) 83:757–62. doi: 10.1111/josh.12091
29. Li YQ, Li XL. The artificial intelligence system for the generation of sports education guidance model and physical fitness evaluation under deep learning. Front Public Health. (2022) 10:917053. doi: 10.3389/fpubh.2022.917053
30. Gentili A, Villani L, Osti T, Corona VF, Gris AV, Zaino A, et al. Strategies and bottlenecks to tackle infodemic in public health: a scoping review. Front Public Health. (2024) 12:1438981. doi: 10.3389/fpubh.2024.1438981
31. Ding Y, Li YH, Cheng L. Application of internet of things and virtual reality technology in college physical education. IEEE Access. (2020) 8:96065–74. doi: 10.1109/ACCESS.2020.2992283
32. Feng YH, You C, Li Y, Zhang Y, Wang Q. Integration of computer virtual reality technology to college physical education. J Web Eng. (2022) 21:2049–72. doi: 10.13052/jwe1540-9589.2173
33. Wang J, Yang Y, Liu H, Jiang L. Enhancing the college and university physical education teaching and learning experience using virtual reality and particle swarm optimization. Soft Computing. (2024) 28:1653–67. doi: 10.1007/s00500-023-09528-4
34. Zhao J, Mao J, Tan J. Global trends and hotspots in research on extended reality in sports: a bibliometric analysis from 2000 to 2021. Digital Health. (2022) 8:20552076221131141. doi: 10.1177/20552076221131141
35. Takayanagi R, Takahashi K, Sogabe T. AI-assisted decision-making and risk evaluation in uncertain environment using stochastic inverse reinforcement learning: American football as a case study. Math Probl Eng. (2022). doi: 10.1155/2022/4451427
36. Almegren A, Mahdi HS, Hazaea AN, Ali JK, Almegren RM. Evaluating the quality of AI feedback: a comparative study of AI and human essay grading. Innov Educ Teach Int. (2024). doi: 10.1080/14703297.2024.2437122
37. Puce L, Ceylan HI, Trompetto C, Cotellessa F, Schenone C, Marinelli L, et al. Optimizing athletic performance through advanced nutrition strategies: can AI and digital platforms have a role in ultra-endurance sports? Biol Sport. (2024) 41:305–13. doi: 10.5114/biolsport.2024.141063
38. Biró A, Cuesta-Vargas AI, Szilágyi L. AI-assisted fatigue and stamina control for performance sports on IMU-generated multivariate times series datasets. Sensors. (2024) 24:132. doi: 10.3390/s24010132
39. Liu L, Ma Y, Huang R, Lin S, Jia M, Liu G, et al. An empirical study of indoor air quality in badminton stadiums in hot summer and cold winter regions of China during spring and fall seasons. Sci Rep. (2024) 14:3427. doi: 10.1038/s41598-024-53996-z
40. Guo Q, Li B. Role of AI physical education based on application of functional sports training. J Intell Fuzzy Syst. (2021) 40:3337–45. doi: 10.3233/JIFS-189373
Keywords: artificial intelligence, colleges and universities, public sports, teaching reform, public health
Citation: Gao Y (2025) The role of artificial intelligence in enhancing sports education and public health in higher education: innovations in teaching models, evaluation systems, and personalized training. Front. Public Health 13:1554911. doi: 10.3389/fpubh.2025.1554911
Received: 03 January 2025; Accepted: 16 April 2025;
Published: 30 April 2025.
Edited by:
José Eduardo Teixeira, Instituto Politécnico da Guarda, PortugalReviewed by:
Pedro Magalhães, Instituto Politécnico de Bragança, PortugalTiago Ferrão Venâncio, Polytechnic Institute of Portalegre, Portugal
Copyright © 2025 Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yan Gao, Z2FveWFuMjAyMkBjdXBlcy5lZHUuY24=