Is generative artificial intelligence catalyzing a paradigmatic transformation in sport science? – Evidence from a mixed-methods systematic review
- 1School of Physical Education and Sports Science, South China Normal University, Guangzhou, China
Abstract
This mixed-methods systematic review synthesises current evidence on the application of generative artificial intelligence (GAI) in sport and exercise science. Following PRISMA guidelines, 64 eligible studies were identified from Web of Science, PubMed, SPORTDiscus, Scopus and other sources, and methodological quality was appraised using JBI critical appraisal tools. Evidence was integrated using bibliometric mapping, Latent Dirichlet Allocation topic modelling, and narrative synthesis. Findings indicate that GAI is increasingly applied across physical education, personalised training, exercise rehabilitation, and health promotion. Two dominant research trajectories were identified: (i) methodological evaluation of GAI-generated content and (ii) application-oriented deployment in real-world practice. Overall, GAI demonstrates moderate to good performance in content generation, guideline adherence, and decision-support efficiency, particularly under structured, high-information prompting. However, most studies remain evaluative or exploratory, with limited high-quality randomised trials, short intervention durations, and insufficient real-world validation. Persistent limitations include inadequate individualisation, instability across outputs, safety concerns in high-risk populations, and emerging ethical challenges related to data governance, bias, and accountability. In conclusion, GAI represents a promising adjunct tool in sport and exercise science, but current evidence is insufficient to support widespread implementation or paradigm-level change. Future research should prioritise rigorous experimental designs, clinically and performance-relevant outcome measures, domain-specific system development, and robust ethical governance frameworks.
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BibTeX
@article{liu2026generative,
title = {{Is generative artificial intelligence catalyzing a paradigmatic transformation in sport science? – Evidence from a mixed-methods systematic review}},
author = {Shunfang Liu and Yongyu Huang and Yupeng Shen},
journal = {Biology of Sport},
year = {2026},
month = aug,
doi = {10.5114/biolsport.2026.162050},
url = {https://doi.org/10.5114/biolsport.2026.162050},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Is generative artificial intelligence catalyzing a paradigmatic transformation in sport science? – Evidence from a mixed-methods systematic review
AU - Shunfang Liu
AU - Yongyu Huang
AU - Yupeng Shen
PY - 2026
DA - 2026/08/24
JO - Biology of Sport
DO - 10.5114/biolsport.2026.162050
UR - https://doi.org/10.5114/biolsport.2026.162050
AB - This mixed-methods systematic review synthesises current evidence on the application of generative artificial intelligence (GAI) in sport and exercise science. Following PRISMA guidelines, 64 eligible studies were identified from Web of Science, PubMed, SPORTDiscus, Scopus and other sources, and methodological quality was appraised using JBI critical appraisal tools. Evidence was integrated using bibliometric mapping, Latent Dirichlet Allocation topic modelling, and narrative synthesis. Findings indicate that GAI is increasingly applied across physical education, personalised training, exercise rehabilitation, and health promotion. Two dominant research trajectories were identified: (i) methodological evaluation of GAI-generated content and (ii) application-oriented deployment in real-world practice. Overall, GAI demonstrates moderate to good performance in content generation, guideline adherence, and decision-support efficiency, particularly under structured, high-information prompting. However, most studies remain evaluative or exploratory, with limited high-quality randomised trials, short intervention durations, and insufficient real-world validation. Persistent limitations include inadequate individualisation, instability across outputs, safety concerns in high-risk populations, and emerging ethical challenges related to data governance, bias, and accountability. In conclusion, GAI represents a promising adjunct tool in sport and exercise science, but current evidence is insufficient to support widespread implementation or paradigm-level change. Future research should prioritise rigorous experimental designs, clinically and performance-relevant outcome measures, domain-specific system development, and robust ethical governance frameworks.
SN - 0860-021X
SN - 2083-1862
ER -