Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: How and when does artificial intelligence use enhance student creativity? The mediating role of knowledge-based dynamic capabilities

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:

As artificial intelligence (AI) becomes more deeply integrated into higher education, understanding its effects on student creativity has become increasingly important. However, the mechanisms through which AI use is associated with student creativity, as well as the boundary conditions of this relationship, remain insufficiently understood. Drawing on the knowledge-based dynamic capabilities (KBDC) framework, this study develops and tests a model that links AI use to student creativity while incorporating the moderating roles of AI self-efficacy and AI trust. Using survey data from 764 university students in China, we employ hierarchical regression analysis to examine the proposed model. The results show that AI use is positively associated with student creativity and that this relationship is mediated by students’ knowledge acquisition, absorptive, and combination capabilities. Furthermore, AI self-efficacy strengthens the positive effects of AI use on all three capability dimensions, whereas AI trust weakens the effects on knowledge acquisition and absorptive capabilities. In doing so, this study advances research on AI use and student creativity by unpacking one important capability-based mediating pathway and the boundary conditions under which this relationship is stronger or weaker. It also contributes to the KBDC literature by extending the application of KBDC theory from the organizational to the individual level and demonstrating how these capabilities operate in AI-enabled learning contexts. The findings offer practical implications for educators, universities, students, and policymakers seeking to foster creative and reflective AI-supported learning.

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

In this study, a questionnaire was used to collect data. Before any data collection began, we obtained ethical approval for this study from the ethics committee of our university (Approval No. CDUTBS-EC-2025-166). Before the formal survey, in order to improve the reliability, validity and clarity of the questionnaire, a small sample of college students in China was pre-surveyed. A total of 120 questionnaires were distributed, and 98 valid questionnaires were recovered, with an effective recovery rate of 81.67%. Combined with the analysis results of the pre-survey data, as well as the constructive opinions put forward by academic experts and interviewees, some items of the first version of the questionnaire were revised, and at the same time, the wording and layout of the remaining items were refined and optimized to improve the quality of the scale.

Limitations and uncertainty · source passage

This study has four limitations that also suggest fruitful directions for future research. First, our data were drawn from university students in China, and the findings may therefore reflect the cultural and institutional specificity of the Chinese higher education context. Prior cross-national research suggests that students’ AI use and attitudes may vary across higher education systems due to cultural and institutional differences (Strzelecki and ElArabawy, 2024; Vaněček et al., 2025). Thus, the extent to which students use AI and the way such use relates to knowledge processing and creativity may be shaped by context-specific factors, such as assessment norms. Future research could apply our model across different national and institutional settings to examine whether these relationships vary under distinct educational norms and AI-use environments.

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

  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

Huainan Wang; Feng Fu; Lei Li; Ming Liu. How and when does artificial intelligence use enhance student creativity? The mediating role of knowledge-based dynamic capabilities. Frontiers in Psychology (2026-07-27). https://doi.org/10.3389/fpsyg.2026.1861541

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.