Actionable companion · Motivation & engagement

Learning with agency: AI anxiety and teacher innovative intentions: a moderated serial mediation model of burnout and creative self-efficacy

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): K–12 · mixed / unspecified. Verify the participant population and task before transferring the findings.

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

Abstract Introduction The proliferation of generative artificial intelligence (AI) in education presents a double-edged sword: while promising transformative pedagogical potential, it simultaneously triggers significant “AI anxiety” among educators. Despite its relevance, the psychological mechanisms through which this specific anxiety impedes teachers’ willingness to innovate remain underexplored. Methods Based on Conservation of Resources (COR) theory and Social Cognitive Theory (SCT), this study constructs a moderated serial mediation model. Data were collected from 528 K-12 teachers via a stratified random sampling method. Structural equation modeling (SEM) and bootstrapping techniques were employed to test the hypotheses. Results Findings indicate that (1) AI anxiety positively predicts job burnout; (2) Job burnout and creative self-efficacy serially mediate the relationship between AI anxiety and innovative intentions (AI Anxiety → Burnout → Lower Creative Self-Efficacy → Reduced Innovative Intentions); and (3) Perceived Organizational Support (POS) acts as a critical moderator, buffering the deleterious effect of AI anxiety on burnout and attenuating the indirect negative effect on innovative intentions. Discussion Moving beyond mere variable relationships, this study reveals a tangible process-based mechanism of teacher resistance to AI: the “depletion → cognition → behavior” pathway. It highlights that anxiety suppresses innovation not merely through emotional exhaustion, but by systematically translating resource depletion into a cognitive deficit (eroded self-efficacy), which ultimately drives behavioral withdrawal. Keywords: AI anxiety, conservation of resources theory, creative self-efficacy, job burnout, social cognitive theory, teacher innovation

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: K–12 · mixed / unspecified. 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 section delineates the methodological framework employed to empirically test the proposed moderated serial mediation model. It details the research design, sampling procedures, measurement instrumentation, and the analytical strategy adopted to ensure the robustness of the findings.

Limitations and uncertainty · source passage

First, the cross-sectional design of this study fundamentally precludes the establishment of definitive causal inferences. Although we framed our relationships in a directional manner based on robust theoretical antecedents (COR and SCT), the data only confirm statistical associations rather than true causality. We must explicitly acknowledge the potential for alternative explanations, including reverse causality. For instance, it is highly plausible that teachers who inherently possess lower creative self-efficacy or are already experiencing burnout may perceive new AI technologies as more threatening, thereby reporting higher levels of AI anxiety. Furthermore, the relationships among these variables are likely dynamic and reciprocal over time, potentially forming a continuous feedback loop where anxiety and withdrawal reinforce each other. Future research must expand on these limitations by employing longitudinal cross-lagged panel models or experience-sampling methods (diary studies) to capture the dynamic, day-to-day unfolding of these psychological states and to rigorously disentangle the causal directionality of the “depletion-cognition-behavior” mechanism.

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

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. Verify the grades and participant ages in each included study before selecting a classroom pilot. Use teacher-led hints and preserve a non-AI route; a K–12 label does not establish the same benefit across every school year. 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. Compare independent learning, delayed retention and transfer within the locally selected grade band. Report age-stage uncertainty rather than pooling young children and adolescents as one population. 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

Tongqiang Dong; Ziyi Yang; Yu Fang; Jianying Che; Lanxin Li; Yong Kong. AI anxiety and teacher innovative intentions: a moderated serial mediation model of burnout and creative self-efficacy. Frontiers in Psychology (2026-05-28). https://doi.org/10.3389/fpsyg.2026.1828689

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.