Generative AI · Education leadership

Actionable companion · Staff capability and AI literacy

From evidence to district action: The chain-mediating roles of generative AI literacy and TPACK in the relationship between AI support and teaching practice among university teachers

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 The rapid development of generative artificial intelligence (AI) is reshaping the higher education ecosystem, and the application of AI in teaching is becoming increasingly widespread. However, many teachers still use AI primarily to improve work efficiency rather than integrating it meaningfully into teaching. This study investigated the relationships among AI support, generative AI literacy, TPACK, and teaching practice among university teachers in Shandong Province, China. Methods A structured questionnaire was administered online via Wenjuanxing, yielding 578 valid responses. Data were analyzed using descriptive statistics, Pearson correlation analysis, confirmatory factor analysis (CFA), and structural equation modeling (SEM). Results The results showed that all variables were significantly and positively correlated. AI support had a significant positive effect on both generative AI literacy and teaching practice. Generative AI literacy had a significant positive effect on teachers’ TPACK, and TPACK had a significant positive effect on teaching practice. Moreover, generative AI literacy and TPACK played significant chain-mediating roles in the relationship between AI support and teaching practice. Conclusion The findings extend the application of the TPACK framework in AI-enhanced educational contexts and provide empirical evidence for higher education administrators and teachers to strengthen teachers’ generative AI literacy and improve technology-integrated teaching practices. Keywords: AI support, generative AI literacy, higher education, teaching practice, TPACK

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. Run a staff workshop using three authentic lesson-planning tasks. Ask participants to identify hallucinations, revise outputs and document when they reject the tool. 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. Measure error-detection accuracy before and after training, preparation time and the quality of the final lesson plans. 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

Zhiyuan Yang; Ya’nan Lan. The chain-mediating roles of generative AI literacy and TPACK in the relationship between AI support and teaching practice among university teachers. Frontiers in Psychology (2026-07-30). https://doi.org/10.3389/fpsyg.2026.1932185

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.