Evidence map›Paper›PMID 42495222›Full record

ArticleFrontiers in psychology2026

Exploring university physical education teachers' artificial intelligence use intention profiles: a Q-methodology study.

Xu Sun, Dinghua Liu, Xinyang Li, Nina Gu, Zuguo Tian

Abstract read
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Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xu SunSchool of Physical Education, Hunan University, Changsha, China.
Dinghua LiuSchool of Physical Education, Hunan Normal University, Changsha, China.
Xinyang LiSchool of Physical Education, Hunan University of Arts and Science, Changde, China.
Nina GuSchool of Physical Education, Hunan University, Changsha, China.
Zuguo TianSchool of Physical Education, Hunan University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence is increasingly entering university teaching, research, and administrative work. This study used Q methodology to examine university physical education teachers' AI use intention profiles and to clarify how they position AI within teaching, research, and professional practice. Methods: A 42-statement Q-set was developed from theoretical models, empirical literature, policy texts, and expert interviews. Forty-five Chinese university physical education teachers completed an online Q-sorting task through Q-sortware, ranking the statements from -5 to +5 and providing explanations for their extreme choices. The Q-sorts were analyzed using Ken-Q Analysis with centroid factor extraction and varimax rotation. Results: The analysis revealed four distinct profiles with different understandings of AI use in physical education: (F1) AI as a practical assistant-improving teaching efficiency, (F2) AI within embodied boundaries-preserving professional judgment, (F3) AI for research support-enhancing academic productivity, and (F4) AI brings more risks than relief-resisting added burdens. These profiles indicate that teachers did not simply accept or reject AI, but located its value and risks differently across routine teaching support, embodied classroom judgment, academic production, and added technological burden. Discussion: These findings show that university physical education teachers' AI use intention is shaped by different professional orientations toward teaching efficiency, embodied judgment, research support, and perceived risk. Consequently, AI training and institutional support should be differentiated according to teachers' work contexts, with greater attention to the embodied, safety-sensitive, and professionally accountable nature of physical education teaching.

Indexed as

AI use intentionartificial intelligenceQ methodologyteacher subjectivityuniversity physical education teachers

Identifiers

PMID42495222
PMCPMC13392487

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LicenceCC BY
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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.