Biology of Sport

Is generative artificial intelligence catalyzing a paradigmatic transformation in sport science? – Evidence from a mixed-methods systematic review

Shunfang Liu 1
,
Yongyu Huang 1
,
Yupeng Shen 1

  1. School of Physical Education and Sports Science, South China Normal University, Guangzhou, China

Biol Sport. 2026;43:1693–1712

DOI: https://doi.org/10.5114/biolsport.2026.162050
Data publikacji online: 2026/08/24
Article files
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Confronting perimenopausal women’s knowledge of coronary heart disease with their health behaviours. Controversial role of hormone replacement therapy in the protection of coronary heart disease
  1. Sengar SS, Hasan AB, Kumar S, et al. Generative artificial intelligence: a systematic review and applications. Multimed Tools Appl. 2024; 84(21):23661–23700.
  2. Kim S, Oh D. Evaluating creativity: can LLMs be good evaluators in creative writing tasks? Appl Sci (Basel). 2025; 15(6):2971.
  3. Zhang H, Xu T, Li H, et al. StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV); 2017:5907–5915.
  4. Xie F, Zeng D, Shen Q, Tang B. A comprehensive survey on text-to-video generation. Chin J Electron. 2025; 34(4):1009–1036. doi: 10.23919 /CJE.2024.00.151.
  5. Lane AM, Kreider RB. Breaking barriers in interdisciplinary research: the case for a unified approach in sports science and public health. Sports (Basel). 2025; 13(3):82.
  6. Hwang GJ, Chen NS. Exploring the potential of generative artificial intelligence in education: applications, challenges, and future research directions. J Educ Technol Soc. 2023; 26(2).
  7. Wang P, Jing Y, Shen S. A systematic literature review on the application of generative artificial intelligence (GAI) in teaching within higher education: instructional contexts, process, and strategies. Internet High Educ. 2025; 100996.
  8. Rao SJ, Isath A, Krishnan P, et al. ChatGPT: a conceptual review of applications and utility in the field of medicine. J Med Syst. 2024; 48(1):59.
  9. Cosma C, Radi A, Cattano R, et al. Exploring chatbot contributions to enhancing vaccine literacy and uptake: a scoping review of the literature. Vaccine. 2025; 44:126559.
  10. Washif JA, Pagaduan J, James C, Dergaa I, Beaven CM. Artificial intelligence in sport: exploring the potential of using ChatGPT in resistance training prescription. Biol Sport. 2024; 41(2):209–220. doi: 10.5114/ biolsport.2024.132987.
  11. Yang Z, Zhang X, Li H, et al. More details, less variability? A crossover design study on the impact of information granularity on ChatGPT’s training program stability. Biol Sport. 2026; 43(1):379–392.
  12. Gürses ÖA, Özüdoğru A, Tuncay F, et al. The role of artificial intelligence large language models in personalized rehabilitation programs for knee osteoarthritis: an observational study. J Med Syst. 2025; 49(1):73.
  13. Sawamura S, Kohiyama K, Takenaka T, et al. Performance of ChatGPT 4.0 on Japan’s National Physical Therapist Examination: a comprehensive analysis of text and visual question handling. Cureus. 2024; 16(8).
  14. Vieira C, Alves J, Alves P, et al. Patient-specific AI-assisted narrative design for serious games targeting motor rehabilitation. In: Proceedings of the 2024 IEEE 3rd International Conference on Intelligent Reality (ICIR); 2024:1–4.
  15. Ha T, Yu H, Lim B, et al. Using ChatGPT in the field of kinesiology: opportunities and considerations. J Phys Educ Sport. 2024; 24(1):3–12.
  16. Puce L, Bragazzi NL, Curra A, et al. Harnessing generative artificial intelligence for exercise and training prescription: applications and implications in sports and physical activity—a systematic literature review. Appl Sci (Basel). 2025; 15(7).
  17. Liu M, Zhang W, Wang J, et al. Promoting active health with AI technologies: current status and prospects of high-altitude therapy, simulated hypoxia, and LLM-driven lifestyle rehabilitation approaches. BioSci Trends. 2025; 19(3):252–265.
  18. Pluye P, Hong QN. Combining the power of stories and the power of numbers: mixed methods research and mixed studies reviews. Annu Rev Public Health. 2014; 35(1):29–45.
  19. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372.
  20. Banh L, Strobel G. Generative artificial intelligence. Electron Mark. 2023; 33(1):63.
  21. Porritt K, Evans C, Bennett C, Loveday H, Bjerrum M, Salmond S, Munn Z, Pollock D, Pang D, Vineetha K, Seah Betsy, Lockwood C. Systematic reviews of qualitative evidence. In: Aromataris E, Lockwood C, Porritt K, Pilla B, Jordan Z, eds. JBI Manual for Evidence Synthesis. JBI; 2024. Accessed February 6, 2026. https://synthesismanual.jbi.global . doi: 10.46658/JBIMES-24-02.
  22. Tufanaru C, Munn Z, Aromataris E, Campbell J, Hopp L. Systematic reviews of effectiveness. In: Aromataris E, Munn Z, eds. JBI Manual for Evidence Synthesis. JBI; 2020. Accessed February 6, 2026. https://synthesismanual.jbi.global . doi: 10.46658/JBIMES-20-04.
  23. Aromataris E, Stern C, Lockwood C, et al. JBI series paper 2: tailored evidence synthesis approaches are required to answer diverse questions: a pragmatic evidence synthesis toolkit from JBI. J Clin Epidemiol. 2022; 150:196–202.
  24. Siddiqui AA, Alshammary F, Mulla M, et al. Prevalence of dental caries in Pakistan: a systematic review and meta-analysis. BMC Oral Health. 2021; 21(1):450.
  25. Washif J, Trabelsi K, Pagaduan J, et al. Changes in physical fitness and body composition of athletes after the COVID-19 lockdown: a systematic review, meta-analysis, and meta-regression, with assessment of the certainty of evidence. Biol Sport. 2026; 43(1):463–488.
  26. Chen C. The CiteSpace Manual. Coll Comput Inform. 2014; 1(1):1–84.
  27. Hassan-Montero Y, De-Moya-Anegón F, Guerrero-Bote VP. SCImago Graphica: a new tool for exploring and visually communicating data. Prof Inf. 2022; 31(5). doi: 10.3145/epi.2022.sep.02.
  28. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003; 3:50–57.
  29. Rashed M, Piorkowski J, McCulloh I. Evaluation of extremist cohesion in a darknet forum using ERGM and LDA. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE/ACM; 2019:899–902.
  30. Odden TOB, Marin A, Caballero MD. Thematic analysis of 18 years of physics education research conference proceedings using natural language processing. Phys Rev Phys Educ Res. 2020; 16(1):010142.
  31. Sievert C, Shirley K. LDAvis: a method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. 2014:63–70.
  32. Popay J, Roberts H, Sowden A, et al. Guidance on the conduct of narrative synthesis in systematic reviews. ESRC Methods Programme. 2006; 1(1):b92.
  33. Albaloul O, Killian C. Evaluating ChatGPT accuracy in answering a basketball common content knowledge test in physical education. Educ Inf Technol. 2026:1–16.
  34. Genç N. Artificial intelligence in physical education and sports: new horizons with ChatGPT. Akdeniz Spor Bilimleri Dergisi. 2023; 6(1):17–32.
  35. Ma W, Liu Y, Yi Q, et al. Table tennis coaching system based on a multimodal large language model with a table tennis knowledge base. PLoS One. 2025; 20(2):e0317839.
  36. Dekerlegand R, Bell A, Clancy MJ, et al. Generative artificial intelligence in education: insights from rehabilitation sciences students. Educ Sci. 2025; 15(3):380.
  37. Prosen M, Ličen S. Perceptions, ethical challenges and sustainable integration of generative AI in health science education: a cross-sectional study. Sustainability. 2025; 17(14):6546.
  38. Wu Q, Li S, Xin S, et al. A study on students’ behavioural intention and use behaviour of artificial intelligence-generated content in physical education: employing an extended unified theory of acceptance and use of technology model. J Hosp Leis Sport Tour Educ. 2025; 36:100547.
  39. Kohiyama K, Sawamura S, Nagai T, et al. Exploratory evaluation of learning behaviors using a ChatGPT-based question-generating bot among occupational therapy students preparing for the national licensure exam: a one-month pre-post study with cluster analysis. Cureus. 2025; 17(10).
  40. Miao C, Zhang S, Liu K. Comparing AI-assisted and traditional tactical instruction: a crossover experimental study among male college students. Acta Psychol (Amst). 2025; 259:105431.
  41. Qiu Y. The impact of LLM hallucinations on motor skill learning: a case study in badminton. IEEE Access. 2024.
  42. Sapre T, Miller HL, Schwartz A, et al. Enhancing home-based physical activity for neurodivergent children: adapting the InPACT at Home program with AI and universal design. Front Physiol. 2025; 15:1496114.
  43. Chang SH, Chen SY, Chang CH. Exploring opportunities and challenges toward ChatGPT for inclusion in sport education. J Hosp Leis Sport Tour Educ. 2025; 37:100572.
  44. Biró A. Gamif‍ication, Gen AI and reinforcement learning as motivational assets in performance sports. In: Proceedings of the 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE; 2024:315–320.
  45. Fan M, Lipowski M, Wang X, Liu T. The effectiveness of healthcare interventions for high-intensity interval training under generative artificial intelligence technology. J Mech Med Biol. 2025; 25.
  46. Havers T, Jelonnek C, Masur L, et al. A professional assessment of training plans for muscle hypertrophy and maximal strength developed by generative artificial intelligence. Biol Sport. 2025; 42(4):353–380.
  47. Havers T, Masur L, Isenmann E, et al. Reproducibility and quality of hypertrophy-related training plans generated by GPT-4 and Google Gemini as evaluated by coaching experts. Biol Sport. 2025; 42(2):289–329.
  48. Puce L, Żmijewski P, Cotellessa F, et al. The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming. Biol Sport. 2026; 43(1):355–367.
  49. Erol E, Arıkan H. Does ChatGPT provide comprehensive and accurate information regarding the effects, types and programming of core exercises? Turk J Kinesiol. 2024; 10(3):178–182.
  50. Düking P, Sperlich B, Voigt L, et al. ChatGPT generated training plans for runners are not rated optimal by coaching experts, but increase in quality with additional input information. J Sports Sci Med. 2024; 23(1):56.
  51. Pajo LS, Rabuya R, Andacao A, et al. A 10-week large language model-generated (llm) versus human-made volleyball training program on the jumping performance of collegiate volleyball athletes. J Phys Educ. 2025; 36:e3611.
  52. Masagca RC. The AI coach: a 5-week AI-generated calisthenics training program on health-related physical fitness components of untrained collegiate students. J Hum Sport Exerc. 2025; 20(1):39–56.
  53. Wachholz F, Manno S, Schlachter D, et al. Acceptance and trust in AI-generated exercise plans among recreational athletes and quality evaluation by experienced coaches: a pilot study. BMC Res Notes. 2025; 18(1):112.
  54. Yang J, Qin S, Ren DM. Artificial intelligence coaches on the sidelines: evaluating readability and quality of soccer training plans from six generative models. Int J Sports Sci Coach. 2025; 17479541251369593.
  55. Shin D, Hsieh G, Kim YH. PlanFitting: personalized exercise planning with large language model–driven conversational agent. In: Proceedings of the 7th ACM Conference on Conversational User Interfaces. 2025:1–19.
  56. Li J, Abouzahir S, El Saddik A. Integrating generative AI for enhanced fitness coaching: from exercise form to posture and body composition analysis. In: Proceedings of the 2025 IEEE Medical Measurements & Applications Conference (MeMeA). IEEE; 2025:1–5.
  57. Vahdati M, Gholizadeh HamlAbadi K, Laamarti F, et al. A multi-agent digital twin framework for AI-driven fitness coaching. In: Proceedings of the 2025 ACM International Conference on Interactive Media Experiences. 2025:380–385.
  58. Zhang J, Han D, Han S, et al. ChatMatch: Exploring the potential of hybrid vision–language deep learning approach for the intelligent analysis and inference of racket sports. Comput Speech Lang. 2025; 89:101694.
  59. Villarreal-Espinosa JB, Berreta RS, Allende F, et al. Accuracy assessment of ChatGPT responses to frequently asked questions regarding anterior cruciate ligament surgery. Knee. 2024; 51:84–92.
  60. Quinn M, Milner JD, Schmitt P, et al. Artificial intelligence large language models address anterior cruciate ligament reconstruction: superior clarity and completeness by Gemini compared with ChatGPT-4 in response to American Academy of Orthopaedic Surgeons clinical practice guidelines. Arthroscopy. 2024.
  61. Safran E, Yildirim S. A cross-sectional study on ChatGPT’s alignment with clinical practice guidelines in musculoskeletal rehabilitation. BMC Musculoskelet Disord. 2025; 26(1):411.
  62. McBee JC, Han DY, Liu L, et al. Assessing ChatGPT’s competency in addressing interdisciplinary inquiries on Chatbot uses in sports rehabilitation: simulation study. JMIR Med Educ. 2024; 10(1):e51157.
  63. Bu L, Qu J, Ding J, Wang C, Li X, Shi P. An adaptive design approach for AIGC-based VR rehabilitation training systems. J Eng Des. 2025:1–26. doi: 10 .1080/09544828.2025.2540239.
  64. Saglam S, Uludag V, Karaduman ZO, et al. Comparative evaluation of artificial intelligence models GPT-4 and GPT-3.5 in clinical decision-making in sports surgery and physiotherapy: a cross-sectional study. BMC Med Inform Decis Mak. 2025; 25(1):163.
  65. Yang Q, Nie R, Zeng H, et al. ExPose: a web-based platform for AI-assisted generation of exercise illustrations for rehabilitation prescriptions. In: Proceedings of the 2024 14th International Conference on Information Technology in Medicine and Education (ITME). IEEE; 2024:980–984.
  66. Sun J, et al. Research on rehabilitation exercise guidance system based on action quality assessment. In: Proceedings of the 2024 17th International Convention on Rehabilitation Engineering and Assistive Technology (i-CREATe). Shanghai, China; 2024:1–5.
  67. Tang J, Abedi A, Colella TJF, et al. Rehabilitation exercise quality assessment and feedback generation using large language models with prompt engineering. In: Proceedings of the International Joint Conference on Artificial Intelligence. Singapore: Springer Nature Singapore; 2025:60–75.
  68. Xing Q, Xing X, Guo P, Tang Z, Shen Y. LLM-FMS: a fine-grained dataset for functional movement screen action quality assessment. PLoS One. 2025; 20(3):e0313707. doi: 10.1371 /journal.pone.0313707.
  69. Chung SM, Chang MC. Assessment of the information provided by ChatGPT regarding exercise for patients with type 2 diabetes: a pilot study. BMJ Health Care Inform. 2024; 31(1):e101006.
  70. Zaleski AL, Berkowsky R, Craig KJT, et al. Comprehensiveness, accuracy, and readability of exercise recommendations provided by an AI-based chatbot: mixed methods study. JMIR Med Educ. 2024; 10(1):e51308.
  71. Xu Y, Liu Q, Pang J, et al. Assessment of personalized exercise prescriptions issued by ChatGPT 4.0 and intelligent health promotion systems for patients with hypertension comorbidities based on the transtheoretical model: a comparative analysis. J Multidiscip Healthc. 2024:5063–5078.
  72. Li G, Li H, Su Y, et al. GPT-4 as a virtual fitness coach: a case study assessing its effectiveness in providing weight loss and fitness guidance. BMC Public Health. 2025; 25(1):2466.
  73. Saraç H, Ulusoy İT, Alpay J, et al. Evaluating the potential role of AI chatbots in designing personalized exercise programs for weight management. Int J Hum Comput Interact. 2025:1–8.
  74. Kothari A, Khanna U, Shah N, et al. Wellness AI: AI-driven personalized diet and fitness recommendation. In: Proceedings of the 2025 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE; 2025:1–5.
  75. Philuek P, Kusump S, Sathianpoonsook T, et al. The effects of chat GPT generated exercise program in healthy overweight young adults: a pilot study. J Hum Sport Exerc. 2025; 20(1):169–179.
  76. Farías H, González Aroca J, Ortiz D. Chatbot based on large language model to improve adherence to exercise-based treatment in people with knee osteoarthritis: system development. Technologies. 2025; 13(4):140.
  77. Dergaa I, Saad HB, El Omri A, et al. Using artificial intelligence for exercise prescription in personalised health promotion: a critical evaluation of OpenAI’s GPT-4 model. Biol Sport. 2024; 41(2):221–241.
  78. Akrimi S, Schwensfeier L, Düking P, et al. ChatGPT-4o-Generated Exercise Plans for Patients with Type 2 Diabetes Mellitus—Assessment of Their Safety and Other Quality Criteria by Coaching Experts. Sports (Basel). 2025; 13(4):92.
  79. Vitale J, McCall A, Cina A, et al. “Can We Trust Them?” An Expert Evaluation of Large Language Models to Provide Sleep and Jet Lag Recommendations for Athletes. Sports Med. 2025:1–14.
  80. Solomon TPJ, Laye MJ. The sports nutrition knowledge of large language model (LLM) artificial intelligence (AI) chatbots: An assessment of accuracy, completeness, clarity, quality of evidence, and test-retest reliability. PLoS One. 2025; 20(6):e0325982.
  81. Larbi D, Løvas SE, Årsand E, et al. Designing and testing a physical activity app with a ChatGPT-based chatbot. In: Intelligent Health Systems—From Technology to Data and Knowledge. IOS Press; 2025:1155–1159.
  82. Onan D, Arıkan H, Can İ, et al. Examining the ability of artificial intelligence with ChatGPT-4.0 to create an exercise program: Case scenario examples” lumbar disc herniation, chronic migraine, and urge urinary incontinence”. Turk J Kinesiol. 2025; 11(1):28–44.
  83. Willms A, Liu S. Exploring the feasibility of using ChatGPT to create just-in-time adaptive physical activity mHealth intervention content: case study. JMIR Med Educ. 2024; 10:e51426.
  84. Khasentino J, Belyaeva A, Liu X, et al. A personal health large language model for sleep and fitness coaching. Nat Med. 2025:1–10.
  85. Kamnerddee C, Putjorn P, Intarasirisawat J. AI-driven design thinking: a comparative study of human-created and AI-generated UI prototypes for mobile applications. In: Proceedings of the 2024 8th International Conference on Information Technology (InCIT). IEEE; 2024:237–242.
  86. Priyanka SP, Janit S. Personal workout trainer using MediaPipe and OpenCV for real-time guidance and feedback. In: Proceedings of the 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE; 2025:1438–1442.
  87. Chittimalla SK, Potluri LKM. Enhancing yoga pose recognition using large language models with real-world dataset integration for personalized fitness assistance. In: Proceedings of the 2024 International Conference on IT Innovation and Knowledge Discovery (ITIKD). IEEE; 2025:1–6.
  88. Bhattacharya H, Kalia R, Yadav SS. AI gym trainer and fitness tracker using GPT-LLM and prompting techniques. In: Proceedings of the 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE; 2024:249–254.
  89. Arumilli A, Oberoi A, Rifa A. An approach for extraction of relevant contextual information from exercise logs using large language models. In: Proceedings of the International Conference on Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing. Cham: Springer Nature Switzerland; 2024:367–380.
  90. Bak M, Chin J. The potential and limitations of large language models in identification of the states of motivations for facilitating health behavior change. J Am Med Inform Assoc. 2024; 31(9):2047–2053.
  91. Fu TH, Wu KC. A dynamic fitness game content generation system based on machine learning. In: Proceedings of the International Conference on Human- Computer Interaction. Cham: Springer Nature Switzerland; 2023:50–62.
  92. Ward H, Toledano MB, Shaddick G, et al. Oxford Handbook of Epidemiology for Clinicians. OUP Oxford; 2012.
  93. Hersh W. Generative artificial intelligence: implications for biomedical and health professions education. Annu Rev Biomed Data Sci. 2025; 8.
  94. Yu L, Alégroth E, Chatzipetrou P, et al. Measuring the quality of generative AI systems: Mapping metrics to quality characteristics-Snowballing literature review. Inf Softw Technol. 2025:107802.
  95. Ha T, Chey WS, Fan X, et al. Technology use in physical education: insights from New York State teachers. J Teach Phys Educ. 2025; 1(aop):1–8.
  96. Waśkiewicz Z, Słomka KJ, Grzywacz T, et al. Ethical bias in AI-driven injury prediction in sport: a narrative review of athlete health data, autonomy and governance. AI. 2025; 6(11):283.
  97. Suo X, Tang W, Li Z. Motion capture technology in sports scenarios: a survey. Sensors. 2024; 24(9):2947.
  98. Das BC, Amini MH, Wu Y. Security and privacy challenges of large language models: a survey. ACM Comput Surv. 2025; 57(6):1–39.
  99. Rush C, Osborne B. Benefits and concerns abound, regulations lack in collegiate athlete biometric data collection. J Legal Aspects Sport. 2022; 32:62.
  100. Hagendorff T. Mapping the ethics of generative AI: a comprehensive scoping review. Minds Mach. 2024; 34:39. doi: 10.1007/s11023-024-09694-w.
  101. MacLean E. The case of tracking athletes’ every move: biometrics in professional sports and the legal and ethical implications of data collection. Sports Law J. 2021; 28:49.
  102. Purcell RH, Rommelfanger KS. Biometric tracking from professional athletes to consumers. Am J Bioeth. 2017; 17(1):72–74.
  103. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25(1):44–56.
  104. Van Dis EAM, Bollen J, Zuidema W, et al. ChatGPT: five priorities for research. Nature. 2023; 614(7947):224–226.
  105. Cirillo D, Catuara-Solarz S, Morey C, et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ Digit Med. 2020; 3(1):81.
  106. Shneiderman B. Human-centered artificial intelligence: reliable, safe & trustworthy. Int J Hum Comput Interact. 2020; 36(6):495–504.
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