Actionable companion · Feedback & scaffolding

Learning with agency: AIGC affordance and student self-regulation in private undergraduate education: a serial mediation model of self-efficacy and learning motivation

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:

Introduction While AIGC tools are reshaping student learning, the psychological mechanisms through which AIGC affordance translates into adaptive learning behaviors remain underexplored. Grounded in Affordance-Actualization Theory and self-regulation theory, this study examines how AIGC affordance activates self-regulated learning (SRL) through the serial mediation of self-efficacy and motivation, contextualized within industry-education integration (IEI) programs in Guangzhou Private Undergraduate. Methods A pragmatic mixed-methods design integrated quantitative surveys (N = 689) with qualitative interviews (N = 8 instructors). PLS-SEM tested the hypothesized psychological pathways; thematic analysis elucidated implementation boundary conditions. Results The results demonstrate that perceived AIGC affordance significantly enhances students’ AIGC self-efficacy, which in turn boosts their learning motivation and ultimately strengthens their self-regulated learning (SRL) capabilities. While the quality of AIGC assessment feedback is a strong predictor of student satisfaction, it does not directly improve SRL; its influence is channeled primarily through motivation. Complementing these findings, interview data uncovered persistent gaps in IEI implementation, including curriculum-industry misalignment, limited project diversity, and weak enterprise engagement, which contextualize the quantitative model. Discussion By integrating SRL theory with the IEI framework, this study reveals that AIGC’s primary value lies not merely as a functional productivity tool but as a psychological catalyst. It fosters adaptive learning by building students’ confidence and motivational drive. The non-significant direct path from feedback quality to self-efficacy and from satisfaction to SRL suggests that positive experience alone is insufficient for deep learning; the activation of motivational and self-efficacy mechanisms is crucial. Conclusion The study concludes that the successful integration of AIGC in IEI requires a dual focus: strategically addressing systemic implementation gaps at the program level, and pedagogically designing AIGC activities that explicitly target the enhancement of self-efficacy and motivation to promote self-regulation.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Qualitative / mixed methods

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 adopts an interpretive sequential mixed-method design: a quantitative survey was conducted from October to November 2024 (including a 4-week IEI program, such as enterprise real-time Double Eleven marketing operation, software development tasks), and qualitative interviews were also carried out to explain the background of the non-significant path. This method combines the universality of quantitative research with the depth of qualitative research (Figure 1).

Limitations and uncertainty · source passage

Limitations and future directions. The proposed serial mediation rests on theoretical temporal logic rather than empirical sequencing. Alternative causal configurations remain plausible: high-SRL students may selectively perceive affordances (reverse causality), or self-efficacy and motivation may reciprocally reinforce. Cross-sectional data preclude strong causal claims; longitudinal or experimental designs—such as manipulating affordance salience through structured versus open-ended AIGC interfaces—are needed to adjudicate these possibilities.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
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

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

Shi-Zhu Liang; Zhi-Cheng Li; Yu-Ming Hsu; Jia-Lu Xu. AIGC affordance and student self-regulation in private undergraduate education: a serial mediation model of self-efficacy and learning motivation. Frontiers in Psychology (2026-09-11). https://doi.org/10.3389/fpsyg.2026.1800950

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.