Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Enhancing student creativity in AIGC design education: the mediating role of learning motivation in reverse engineering pedagogy

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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

Abstract Introduction The widespread application of artificial intelligence generated content (AIGC) technology in design education presents challenges in balancing technological dependency and autonomous innovation during creativity development. Establishing a dynamic equilibrium between technological empowerment and innovation capacity is critical in educational practice. This study examines how reverse engineering pedagogy (REP) stimulates learning motivation to foster creativity and proposes a multidimensional creativity evaluation framework. Methods A quasi-experimental design with intact class-based quasi-random allocation was employed, involving 80 first-year animation majors from two intact classes (40 students per class) assigned to an experimental group (REP intervention) and a control group (traditional instruction). Quantitative assessments using multidimensional creativity and learning motivation scales, supported by statistical analysis, revealed inter-variable relationships. Results The results indicated that the REP framework, comprising structural analysis, functional mapping, and creative reconstruction, significantly enhanced creative design thinking. Learning motivation partially mediated the effect of REP on creativity, confirming synergistic influences of intrinsic and extrinsic motivation. Discussion The study proposes a multidimensional creativity evaluation framework integrating REP and learning motivation, and provides theoretical-practical guidance for pedagogical innovation in AIGC-driven design education. Keywords: AIGC, creativity, design education, learning motivation, REP

What kind of evidence is this?

Supporting AI learning science · Randomized study reported

Stage: Age / stage not established. 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 quasi-experimental design with intact class-based allocation was conducted in an Animation Scene Design course at a university in Nanjing, China, to evaluate the effectiveness of REP on student creativity in AIGC-driven design education. Participants comprised 80 first-year animation majors (aged 18-20) from two intact classes, with 40 students in each class. The two classes were assigned to either an experimental group (EG) receiving REP-based intervention (n = 40; 18 male, 45%; 22 female, 55%) or a control group (CG) exposed to traditional instruction (n = 40; 19 male, 47.5%; 21 female, 52.5%) using quasi-random allocation. All participants had basic drawing and design skills but no prior formal training in animation scene design.

Limitations and uncertainty · source passage

1) REP significantly enhances student creativity in AIGC design education. Students in the experimental group outperformed those in the control group across all dimensions of creativity. REP helps overcome the limitations of imitative learning and fosters original design thinking.

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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Gang Liu; Xu Zhang. Enhancing student creativity in AIGC design education: the mediating role of learning motivation in reverse engineering pedagogy. Frontiers in Psychology (2026-06-19). https://doi.org/10.3389/fpsyg.2026.1829753

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.