Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: The Role of Generative Artificial Intelligence in Shaping University Students’ Learning Behavior: A Mixed-Method Research Based on the COM-B Model

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 While GenAI is transforming education, it remains unclear how it shapes students’ behavior, especially concerning AI literacy. The purpose of this study is to examine which factors positively affect students’ learning behavior and whether AI literacy moderates this effect, using the COM-B model. An online survey of 438 participants was analyzed using covariance-based structural equation modeling (CB-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The CB-SEM results indicate that independent learning ability, receptive ability, learning environment, AI support equipment, and both intrinsic and extrinsic motivations significantly shape student learning behavior. Notably, AI literacy moderates the relationship between GenAI and learning behavior. Furthermore, fsQCA reveals seven configurations of these factors that favorably impact learning behavior. Together, these findings provide theoretical and practical insights for universities, highlighting actionable ways universities can support students’ adoption of GenAI. Keywords: generative artificial intelligence, university students, learning behaviors, AI literacy, the COM-B model, CB-SEM, fsQCA

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

Rui Ma; Mingfei Guo. The Role of Generative Artificial Intelligence in Shaping University Students’ Learning Behavior: A Mixed-Method Research Based on the COM-B Model. Behavioral Sciences (2026-04-11). https://doi.org/10.3390/bs16040577

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.