Actionable companion · Assessment and evidence of learning
From evidence to district action: Teacher-regulated generative AI support, student agency, and perceived learning gains in higher education: the moderating role of perceived fairness
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.
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 Introduction Generative artificial intelligence is increasingly used in higher education, yet its educational value depends not only on technological access but also on how its use is pedagogically regulated. This study examined how teacher-regulated generative AI support is associated with university students' perceived learning gains in higher education. It further tested whether student agency mediates this association and whether perceived fairness conditions the strength of the association between teacher-regulated generative AI support and student agency. Methods We adopted a cross-sectional quantitative survey design and collected data from 428 university students from six universities in western China who had used generative AI in course-related learning tasks under teacher guidance and within teacher-defined expectations. Structural equation modeling was used to test the direct and indirect associations in the proposed model, and a manifest interaction analysis was used to examine the first-stage moderation effect of perceived fairness. Results In the proposed model, teacher-regulated generative AI support was positively associated with perceived learning gains both directly and indirectly through student agency. Student agency was statistically consistent with a mediated pathway linking teacher-regulated generative AI support to perceived learning gains. In addition, perceived fairness was associated with a stronger positive relationship between teacher-regulated generative AI support and student agency. Discussion These findings suggest that students reported greater learning benefits when generative AI use was guided more clearly by teachers, taken up more actively by learners, and experienced in a fairer learning context. The study therefore highlights student agency as one plausible pathway linking teacher-regulated AI use to perceived learning gains in higher education. Keywords: Generative AI, higher education, perceived fairness, perceived learning gains, student agency, teacher-regulated generative AI support
A 90-day district pilot
- 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.
- 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.
- 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.
- 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
Zeyu Zhang; Xiaomei Lu; Mahpiret Kanji. Teacher-regulated generative AI support, student agency, and perceived learning gains in higher education: the moderating role of perceived fairness. Frontiers in Artificial Intelligence (2026-07-13). https://doi.org/10.3389/frai.2026.1863666
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.