Generative AI · Education leadership

Actionable companion · Governance, equity and accountability

From evidence to district action: Factors influencing the adoption of generative artificial intelligence into classroom teaching by university teachers: An empirical study using SPSS PROCESS macros

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:

With the development of science and technology, higher education faces the challenge of AIGC. As one of the central bodies of higher education, it is essential to understand whether teachers accept this new technology and what factors influence their choice. This study collected survey data from teachers at 42 universities in China and constructed a structural equation model based on social cognitive theory to explore the factors influencing the adoption of AIGC by university teachers. In this study, partial least squares (PLS) were used to analyze the validity and reliability of the data and process macro model 4 and model 61 based on SPSS software were used to verify the mechanism of the influence of each factor in the structural equation modeling on willingness to choose. It was found that self-efficacy is a key factor in the willing to choose from the perspective of college teachers; the positive influence of self-efficacy on willingness to choose is more significant when the level of outcome expectation is higher; external environmental factors will strengthen the positive influence of self-efficacy on outcome expectation and willing to choose; and the ability of self-improvement will also enhance the positive influence of self-efficacy on outcome expectation. This study provides an in-depth exploration of the critical factors influencing teachers’ adoption of AIGC, providing valuable insights and empirical evidence for decision-making on technology integration in education and providing educational administrators and policymakers with references on how to promote teachers’ adoption of new technologies.

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. Convene a teacher, student, family and technology working group. Test one proposed use against a documented privacy, accessibility, bias and human-review checklist before authorizing a pilot. 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. Track unresolved risks, subgroup access, complaint resolution time and the proportion of outputs that require substantive human correction. 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

Yong Xiang; Chenxin Yang; Zhigang Jin; Wanshu Zhao. Factors influencing the adoption of generative artificial intelligence into classroom teaching by university teachers: An empirical study using SPSS PROCESS macros. PLOS One (2025-08-20). https://doi.org/10.1371/journal.pone.0324875

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.