Evidence map›Paper›PMID 41867984›Full record

ArticleFrontiers in psychology2026

Longitudinal associations between generative artificial intelligence adoption and university PE teachers' professional competence.

Gang Zhou, Qishun Yang, Xiao Chen

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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.

Gang Zhou *School of Football, Wuhan Sports University, Wuhan, Hubei, China.
Qishun Yang *School of Physical Education, Wuhan Business University, Wuhan, Hubei, China.
Xiao ChenSchool of Athletic Training, Tianjin University of Sport, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Generative artificial intelligence (GenAI) is increasingly integrated into educational practice, yet longitudinal evidence regarding its role in teachers' professional development remains limited, particularly in physical education contexts characterized by embodied teaching. Methods: This study employed a three-wave longitudinal design over 1 year to examine the dynamic associations between GenAI adoption and professional competence among 558 university physical education teachers in China. Cross-lagged panel modeling was used to test the longitudinal mediating role of self-regulated learning and the moderating effect of teaching experience. Results: The results showed that GenAI adoption exhibited a significant and sustained positive association with teachers' professional competence across time. Self-regulated learning functioned as a longitudinal mediator linking GenAI adoption to professional competence. Moreover, teaching experience negatively moderated this indirect pathway, such that the mediation effect was stronger among early-career teachers than among those with longer teaching experience. Conclusion: By integrating the Technological Pedagogical Content Knowledge (TPACK) framework with self-regulated learning and career stage perspectives, this study extends prior cross-sectional research by elucidating the dynamic mechanisms through which GenAI adoption is associated with teachers' professional competence development. The findings highlight the importance of differentiated and targeted digital empowerment strategies in physical education.

Indexed as

cross-laggedpanel modelgenerative artificial intelligence adoptionself-regulated learningteaching experienceuniversity physical education teachers

Identifiers

PMID41867984
PMCPMC12999848

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.