Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: The association between AI literacy and creative self-efficacy: a moderated mediation model of learning adaptability and teacher support

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:

Background As artificial intelligence (AI) becomes increasingly integrated into education, fostering student creativity is a critical priority. While prior research suggests a positive link between AI literacy and creative self-efficacy, the underlying psychological mechanisms and key contextual factors remain largely unexplored. This study aimed to address this gap by examining the mediating roles of learning adaptability (cognitive, behavioral, and emotional) and the moderating role of teacher support in this relationship. Methods A total of 509 university engineering students (68.6% male; M age = 19.36 years) participated in a survey. They completed validated measures of AI literacy, learning adaptability, teacher support, and creative self-efficacy. A moderated mediation model was tested using statistical analysis. Results The findings indicated a significant positive association between AI literacy and creative self-efficacy. This relationship was significantly mediated by behavioral and emotional adaptability, but not by cognitive adaptability. Furthermore, teacher support moderated the positive effect of AI literacy on emotional adaptability; this effect was stronger for students who perceived higher levels of teacher support. Conclusion The study advances a nuanced model demonstrating that AI literacy enhances creative confidence primarily by fostering a willingness to act (behavioral adaptability) and bolstering emotional resilience (emotional adaptability). Teacher support is identified as a crucial contextual resource that amplifies this positive process. These findings offer valuable insights for educators and policymakers aiming to design interventions that maximize the creative potential of AI in learning environments.

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

Participants were recruited through convenience sampling from a vocational college in Chengdu, Sichuan Province. The sample consisted of first- and second-year undergraduate students majoring in engineering. Data were collected between September and November 2025. Prior to participation, all individuals were provided with detailed information about the study and informed of their right to withdraw at any time. After obtaining informed consent, participants independently completed an online questionnaire on their smartphones via Wenjuanxing, an online survey platform. All participants received course credit as compensation for their participation. The study was approved by the Institutional Review Board (IRB) of the first author’s institution.

Limitations and uncertainty · source passage

While the present study yields several valuable conclusions, some limitations should be acknowledged that warrant attention in future research. First, this study employed a cross-sectional design, which precludes definitive causal inferences between variables. Although our hypotheses were supported by the data, the temporal precedence among the variables could not be strictly established. Future studies should adopt longitudinal or experimental designs to more rigorously test the moderated mediation model proposed herein. Second, all data were collected through self-report questionnaires, which may be susceptible to social desirability effects or common method bias. For instance, students might have overestimated their AI literacy or creative self-efficacy. Future research should integrate multi-source, multi-method data, such as behavioral observations of students’ AI usage processes or by introducing objective, task-based assessments of AI literacy. This would mitigate the bias of a single method and provide a more accurate depiction of the learning process in AI-enabled environments. Third, the sample of this study consisted solely of university engineering students, which may limit the generalizability of the findings. Engineering students may possess specific characteristics regarding their logical thinking and technology acceptance. Expanding the research sample to include students from diverse academic backgrounds, such as the humanities and arts, as well as from different educational levels and cultural contexts, would be beneficial for testing the cross-contextual stability of the mechanisms identified in this study. Fourth, the present study measured AI literacy as a holistic construct. However, according to the existing literature (Long and Magerko, 2020), AI literacy is a multidimensional competency framework comprising different facets such as usage skills, critical thinking, and ethical understanding. It is plausible that these different dimensions of literacy have differential effects on learning adaptability. Future research could delve deeper into how each sub-dimension of AI literacy functions through distinct adaptability pathways, thereby providing more fine-grained guidance for the cultivation of AI literacy.

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

Lu Pan; Xuezhen Shuang; Yiping Liu. The association between AI literacy and creative self-efficacy: a moderated mediation model of learning adaptability and teacher support. Frontiers in Psychology (2026-07-28). https://doi.org/10.3389/fpsyg.2026.1798750

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.