Actionable companion · Motivation & engagement

Learning with agency: In-class AI usage and university students’ attitudes: The mediating role of perceived learning engagement benefits

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

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

With the widespread adoption of artificial intelligence (AI), the usage of various AI tools by university students to support learning has become increasingly common in classroom settings. Focusing on routine in-class AI usage and learning engagement, this study explores the mediating role of perceived learning engagement benefits in linking students’ AI usage behavior to their attitudes, and investigates whether its mediating effects differ across various dimensions of learning engagement. Based on data from 287 questionnaires, structural equation modeling and Bayesian analysis were used to test the proposed hypotheses. The results indicate that in-class AI usage was associated with students’ attitudes both directly and also indirectly by enhancing cognitive engagement and emotional engagement, both of which exhibited comparable mediating effects. In general, the university students exhibited positive attitudes toward in-class AI usage, and these attitudes were positively associated with their perceived learning engagement benefits. These results suggest that the value of AI in classroom settings may relate not only to learning efficiency, but also to students’ learning experiences, particularly in terms of perceived cognitive and emotional engagement benefits. Notably, although in-class AI usage was positively associated with all dimensions of perceived engagement benefits, perceived behavioral engagement benefits showed no significant indirect effect on attitudes. This null finding warrants further exploration in future research.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

Questionnaire data were collected from 287 students from H University, a research-intensive university in China renowned for its strength in the engineering disciplines. The participants were recruited using convenience sampling. An online questionnaire was disseminated via WJX.cn, a widely used online questionnaire platform in China. The questionnaire was advertised to undergraduate and postgraduate students in various schools and departments of H University via the social media application WeChat. Upon clicking the survey link, participants were presented with a description of the research for them to review, an assurance that their participation would be anonymous, and the specific survey questions. All respondents took part in this survey voluntarily. Of the participants, 169 were males and 118 were females; 180 were undergraduates and 107 were postgraduates; 202 had science and engineering backgrounds and 85 had humanities and social sciences backgrounds.

Limitations and uncertainty · source passage

This study has some limitations worth noting, as they point to directions for future research. First, the participants in the research sample were limited to students from a single research-intensive university in China, and the sample size (N = 287) was relatively small. This may limit the generalizability of the findings to students in other types of institutions or different cultural contexts. Researchers conducting future studies are recommended to extend the sampling scope and conduct comparative investigations with students across different countries.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

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. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. 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 and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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

Wenqiang Fan; Jiaqi Fang; Yanlin Sun; Xinrui Xie; Yaodong Li. In-class AI usage and university students’ attitudes: The mediating role of perceived learning engagement benefits. PLOS One (2026-08-04). https://doi.org/10.1371/journal.pone.0355490

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.