Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: The impact of teachers’ innovative teaching behaviors on vocational college students’ learning engagement in the Chinese context: the mediating roles of AI trust and learning satisfaction

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 In the context of artificial intelligence (AI) integration in higher education—such as AI-supported learning platforms, automated feedback and grading systems, and generative AI tools like ChatGPT—teachers’ innovative teaching behaviors (ITB) are a key factor influencing students’ learning engagement (LE). While previous research has confirmed the positive effect of ITB on LE, the underlying psychological mechanisms, especially in AI-mediated environments, remain insufficiently understood. Guided by student engagement theory and social-cognitive perspectives on technology use, this study examined the parallel mediating roles of trust in AI (AIT) and learning satisfaction (LS) in the relationship between ITB and LE. A survey was conducted among 513 vocational university students in Guangdong, China—a region with advanced digital infrastructure and early AI adoption. The results showed that ITB significantly and positively predicted LE. Both AIT and LS acted as partial mediators in this relationship, suggesting that enhancing students’ satisfaction yields slightly stronger gains in engagement than focusing on AI trust alone. The findings indicate that teachers’ innovative practices enhance engagement both directly and indirectly by fostering learning satisfaction and cultivating calibrated, critical trust in AI—that is, confidence in AI’s usefulness combined with awareness of its limitations. This study is among the first to model AI trust and learning satisfaction as parallel mediators between innovative teaching behaviors and learning engagement, thereby enriching our understanding of how pedagogical innovation and students’ psychological perceptions jointly shape engagement in the AI era. These results imply that higher education should not only encourage innovative, AI-supported teaching designs but also deliberately nurture students’ emotional experiences (satisfaction) and their thoughtful, critical trust in AI tools to maximize learning engagement. Keywords: artificial intelligence trust, innovative teaching behaviors, learning engagement, learning satisfaction, vocational university students

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

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

A total of 513 vocational university students were recruited by convenience sampling from several higher vocational institutions in Guangdong Province, China. Guangdong is a highly developed coastal province in southern China, with relatively advanced digital infrastructure and early adoption of AI in higher education, which makes it a suitable context for studying AI-mediated teaching and learning. The sampling frame consisted of full-time students enrolled in vocational bachelor and higher vocational diploma programs. Inclusion criteria were: (a) currently enrolled in one of the participating institutions; (b) able to complete an online questionnaire in Chinese; and (c) provided informed consent. Questionnaires with excessive missing data or patterned responses were excluded from analysis. Among the 513 valid respondents, 217 (42.3%) were male and 296 (57.2%) were female; Regarding academic year, 157 (30.6%) were first-year students, 167 (32.5%) second-year students, 189 (36.8%) third-year students, which sum to the total valid sample of 513.

Limitations and uncertainty · source passage

Although this study provides valuable findings, several limitations should be acknowledged. First, as described in the literature review, the sample comes from universities in the Guangdong region, and regional characteristics may affect the generalizability of the results. Future research should expand to different regions and types of institutions to test the robustness of these patterns.

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.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

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

Tang Zhiwen; Ying Lin; Che Jing Shang. The impact of teachers’ innovative teaching behaviors on vocational college students’ learning engagement in the Chinese context: the mediating roles of AI trust and learning satisfaction. Frontiers in Psychology (2026-03-11). https://doi.org/10.3389/fpsyg.2026.1646199

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.