Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: Understanding art and design teachers’ willingness to adopt artificial intelligence in teaching under resource constraints: a mixed-methods study on perceived usefulness, resource readiness, and creativity-related concerns

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 Artificial intelligence (AI) is increasingly used in higher education, but teachers’ willingness to adopt AI in creative disciplines may not be fully explained by the instrumental logic of the Technology Acceptance Model (TAM). In art and design education, judgments about AI may also involve concerns about originality, skill development, process-based learning, and pedagogical governance. This study examined art and design teachers’ intentions to adopt AI under resource-constrained conditions. Methods An explanatory sequential mixed-methods design was used. The quantitative phase included 101 valid questionnaires and tested relationships among perceived ease of use (PEOU), perceived usefulness (PU), resource readiness (RR), attitude (AT), and behavioral intention (BI) using structural equation modeling with 5,000 bootstrap replications. The qualitative phase involved interviews with eight art and design teachers to contextualize the quantitative findings. Results Model fit was mixed (CFI = 0.935, TLI = 0.920, SRMR = 0.059, RMSEA = 0.096). Bootstrap results supported PEOU → PU, PU → BI, AT → BI, and RR → PEOU, whereas PU → AT and RR → PU were not supported. PEOU → AT showed a significant negative relationship, contrary to the hypothesized direction. Although RR → AT was positive, its standardized coefficient exceeded 1 and discriminant validity between RR and AT was insufficient; therefore, this path was not substantively interpreted. Interviews indicated that concerns about originality, skill degradation, shortcuts, copyright, academic integrity, and governance may shape how teachers translate functional evaluations into overall attitudes. Discussion Several core TAM pathways were supported, but the model was not fully validated. The findings suggest that AI adoption in art and design teaching involves both instrumental judgments and discipline-specific professional, pedagogical, ethical, and resource considerations. Given limitations in model fit and discriminant validity, the results should be regarded as preliminary and context-specific evidence.

What kind of evidence is this?

Supporting AI learning science · 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

The study focuses on art and design faculty from several universities in central China. The teaching environments of the surveyed instructors vary in terms of AI accounts, course resources, equipment, modular case studies, training opportunities, and institutional regulations, thus this study treats resource availability as a key contextual factor, rather than broadly characterizing the entire region as resource-poor.

Limitations and uncertainty · source passage

This study still has notable limitations. First, a sample size of n = 101 remains relatively small for a CB-SEM model involving five latent variables and eight pathways. Although the study has increased the number of parameter bootstrap replications to 5,000 and conducted a Bollen–Stine bootstrap test, competing CFA, and sample/item sensitivity analyses, these procedures cannot replace independent replication with a larger sample, nor do they confirm adequate sample size; future research should perform model-specific Monte Carlo power analyses based on expected loadings, paths, and distributions. Second, the RMSEA value of 0.096 indicates only partially acceptable overall model fit.

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.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

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

Shu Chen; Xizhen Li; Xiaoting Liu; Jiaxin Wu; Wenhao Cheng. Understanding art and design teachers’ willingness to adopt artificial intelligence in teaching under resource constraints: a mixed-methods study on perceived usefulness, resource readiness, and creativity-related concerns. Frontiers in Psychology (2026-09-03). https://doi.org/10.3389/fpsyg.2026.1854412

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.