Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: AI-supported teaching and student engagement: the mediating role of perceived competence in vocational education

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:

Abstract Introduction Artificial intelligence (AI) is increasingly used in education, yet less is known about the psychological processes through which AI-supported teaching relates to student engagement, especially in vocational education. Drawing on Self-Determination Theory, this study examined the mediating roles of perceived competence and perceived autonomy in the relationship between AI-supported teaching and student engagement. Methods A 10-week quasi-experimental study was conducted with 148 second-year vocational college students, assigned to either an AI-supported teaching group (n = 74) or a conventional instruction group (n = 74). Multimodal data were collected, including academic assessments, performance evaluations, self-report measures of perceived competence, perceived autonomy, and engagement, as well as behavioral records from AI-supported learning activities. Mediation analysis was performed using bootstrapping with 5,000 resamples. Results Students in the AI-supported teaching group showed higher levels of student engagement than those in the conventional instruction group (Cohen's d = 0.84). Perceived competence showed a statistically significant indirect association between AI-supported teaching and student engagement [β = 0.22, 95% CI (0.13, 0.31)], supporting H2. In contrast, perceived autonomy did not show a statistically significant indirect effect [β = 0.02, 95% CI (−0.02, 0.06)], and therefore H3 was not supported. The direct association between AI-supported teaching and engagement remained significant after accounting for both mediators. Discussion These findings suggest that competence-related experiences may represent an important psychological pathway linking AI-supported teaching and student engagement in structured vocational education contexts. The non-significant role of perceived autonomy indicates that motivational processes in AI-supported learning may vary across instructional settings and should be interpreted in relation to contextual and pedagogical conditions. Given the quasi-experimental design and the context-specific sample, the findings should be interpreted cautiously and further examined through larger, multi-site, andlongitudinal studies. Keywords: AI-supported teaching, perceived autonomy, perceived competence, quasi-experimental design, Self-Determination Theory, student engagement, vocational education

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

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

This study employed a quasi-experimental pretest–posttest control group design over a ten-week intervention period. Because individual random assignment was not feasible in authentic classroom settings, intact classes were used as the unit of assignment. Four existing classes were allocated to either the AI-supported teaching condition or the conventional instruction condition using stratification based on institutional context and baseline academic performance to enhance group comparability.

Limitations and uncertainty · source passage

First, the study employed a quasi-experimental design using intact classroom groups rather than individual random assignment. Although baseline equivalence tests and standardized instructional procedures were implemented to reduce potential confounding effects, the design cannot fully eliminate the possibility of instructor effects, classroom-level influences, or novelty effects associated with AI-supported learning.

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.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

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. 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.
  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 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

Linlin Yin; Yi Tang; Xiaoyu Ou. AI-supported teaching and student engagement: the mediating role of perceived competence in vocational education. Frontiers in Psychology (2026-07-13). https://doi.org/10.3389/fpsyg.2026.1870826

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.