Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Longitudinal associations between generative artificial intelligence adoption and university PE teachers’ professional competence

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Confirm the study population, setting and task before applying its findings to your district.

Original authors’ abstract, reproduced under the linked source license:

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. Keywords: cross-laggedpanel model, generative artificial intelligence adoption, self-regulated learning, teaching experience, university physical education teachers

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Gang Zhou; Qishun Yang. Longitudinal associations between generative artificial intelligence adoption and university PE teachers’ professional competence. Frontiers in Psychology (2026-03-05). https://doi.org/10.3389/fpsyg.2026.1775028

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.