Evidence map›Paper›PMID 41311624›Full record

ArticleFrontiers in sports and active living2025

Augmentation or substitution: defining role of large language model in physical education.

Huijun Yang, Bingyi Huang, Xu Li

Abstract read
In one paragraph

Article in Frontiers in sports and active living, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Huijun YangSchool of Physical Education, Collaborative Innovation Team for the Study and Interpretation of Xi Jinping's Important Discourses on Cyberspace Governance, Chongqing University of Posts and Telecommunications, Chongqing, China.
Bingyi HuangSchool of Physical Education, Collaborative Innovation Team for the Study and Interpretation of Xi Jinping's Important Discourses on Cyberspace Governance, Chongqing University of Posts and Telecommunications, Chongqing, China.
Xu LiSchool of Physical Education, Collaborative Innovation Team for the Study and Interpretation of Xi Jinping's Important Discourses on Cyberspace Governance, Chongqing University of Posts and Telecommunications, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Large language model has seen rapid uptake in education alongside advances in artificial intelligence. Its capabilities in areas such as healthcare planning and question answering suggest strong potential for supporting personalised instruction in physical education. At the same time, studies have raised concerns regarding safety, accuracy, and contextual appropriateness. This study examines the role of large language model in physical education and evaluates its suitability as an assistive tool or possible alternative to human instruction. Methods: This study conducted a questionnaire-based evaluation comparing three generative approaches for producing physical education lesson plans and teacher responses: (1) plans written directly by a large language model, (2) plans produced by a large language model after being provided with domain resources, and (3) plans created collaboratively by physical education teachers together with the large language model. Feasibility, practicality, safety, adaptability, and content quality were assessed across these approaches. Large language model-generated responses in typical physical education scenarios were further evaluated in terms of response accuracy, safety, clarity, adaptability, guidance quality, acceptability to learners, and perceived potential to enhance teacher response efficiency. Results: Lesson plans produced collaboratively by physical education teachers and the large language model outperformed those produced solely by the large language model-both with and without additional resources-across all evaluated dimensions. In addition, responses generated by the large language model in physical education scenarios were rated highly for clarity, guidance, adaptability, and support for teacher efficiency. Discussion: The large language model demonstrates clear value in physical education, particularly as a means to support and augment instructional design and responsiveness. However, it does not yet replace the pedagogical judgment, contextual awareness, and safety oversight of human instructors. Overall, this study concludes that the large language model is best positioned as an assistive tool to optimise teaching practice rather than as a standalone replacement for human teachers.

Indexed as

artificial intelligenceassistive toolslarge language modelphysical educationsustainability teaching

Identifiers

PMID41311624
PMCPMC12647000

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.