Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: Preschool teachers’ AI adoption and occupational well-being: an integrated TAM-JD-R analysis of technostress dual-edged effects

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): Early childhood · preschool, Children · stage unspecified. Verify the participant population and task before transferring the findings.

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

Abstract Introduction The rapid integration of generative artificial intelligence (AI) in early childhood education presents both opportunities and challenges for preschool teachers, yet empirical investigations explicating adoption mechanisms and well-being implications remain scarce. This study developed and tested an integrated framework synthesizing the Technology Acceptance Model (TAM) and Job Demands-Resources (JD-R) theory to examine how cognitive evaluations, technostress dimensions, and contextual factors jointly determine AI adoption intentions and occupational well-being among preschool teachers. Methods Survey data from 300 Chinese preschool teachers, recruited via multistage stratified random sampling, were analyzed through covariance-based structural equation modeling. Results Results confirmed TAM’s core pathways, with perceived usefulness (β = 0.365, p < 0.001) and perceived ease of use (β = 0.250, p = 0.001) significantly predicting adoption intentions. The study empirically validated technostress’s dual-edged effects: challenge technostress facilitated adoption (β = 0.182, p = 0.003) while hindrance technostress inhibited it (β = −0.267, p < 0.001). AI adoption intention positively predicted occupational well-being (β = 0.198, p = 0.008). Organizational support buffered hindrance technostress’s negative effects on adoption intention, while workload intensified hindrance technostress’s detrimental impact on well-being. Discussion These findings advance understanding of technology adoption in early childhood education by demonstrating that technostress operates through dual pathways—facilitating and impeding adoption—moderated by organizational and personal resources. Practical implications include targeted support strategies that buffer hindrance stressors while cultivating challenge-oriented appraisals among preschool teachers. Keywords: artificial intelligence adoption, job demands-resources theory, occupational well-being, preschool teachers, structural equation modeling, technology acceptance model, technostress

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Early childhood · preschool; Children · stage 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

Sample size determination followed established guidelines for CB-SEM, which recommend minimum sample sizes of 200 for models with moderate complexity (Jacobucci et al., 2016). Applying the conservative 10:1 ratio of observations to estimated parameters, the study’s 30-item measurement model with nine latent constructs required a minimum of 300 participants. A total of 347 questionnaires were distributed, yielding 312 returns (response rate: 89.9%). After excluding 12 incomplete responses ( 0.80) for detecting medium effect sizes (Cohen’s f2 = 0.15) with α = 0.05.

Limitations and uncertainty · source passage

Practical Implications. The findings inform three stakeholder domains. For administrators, organizational support constitutes a critical intervention target, manifesting through dedicated implementation time, responsive technical assistance, and collaborative learning communities rather than isolated training workshops (Maas et al., 2022). Workload management proves essential, as adding technology expectations without resource reallocation risks transforming potential challenges into hindrances. For policymakers, the heterogeneous technostress responses suggest one-size-fits-all mandates prove inadequate; policies should enable context-responsive implementation pathways accommodating institutional diversity adequate training reduce cognitive load (Sillat et al., 2017; Xiong et al., 2025). For professional development providers, efficacy-building approaches (emphasizing mastery experiences, vicarious learning, and reflective practice) warrant prioritization over purely skill-focused training, given self-efficacy’s foundational role in appraisal processes (Lim, 2023).

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. Use a brief adult-mediated story, turn-taking or language activity. An educator controls every interaction; the agent never becomes an unsupervised companion. Preserve hands-on play and human conversation. 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. Measure independent naming, explanation or turn-taking in a human-led activity after the tool is removed. Record frustration, overstimulation and the amount of adult help. 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

Jia Hanze. Preschool teachers’ AI adoption and occupational well-being: an integrated TAM-JD-R analysis of technostress dual-edged effects. Frontiers in Psychology (2026-04-29). https://doi.org/10.3389/fpsyg.2026.1782464

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.