@Article{Liu2026,
journal="Biology of Sport",
issn="0860-021X",
year="2026",
title="Is generative artificial intelligence catalyzing a paradigmatic transformation in sport science? – Evidence from a mixed-methods systematic review",
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. ",
author="Liu, Shunfang
and Huang, Yongyu
and Shen, Yupeng",
pages="1693--1712",
doi="10.5114/biolsport.2026.162050",
url="http://dx.doi.org/10.5114/biolsport.2026.162050"
}