Agentic education · Learning science

Actionable companion · Cognitive load & attention

Learning with agency: The first scene of artificial intelligence curriculum reform: longitudinal evidence on teachers' stress and multidimensional anxiety

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): Secondary school. Verify the participant population and task before transferring the findings.

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

Abstract Introduction The formal incorporation of artificial intelligence courses into basic education has raised a key issue for the effective implementation of the curriculum: whether teachers can withstand the continuously rising teaching pressure in the early phase of reform and prevent such pressure from further developing into teaching anxiety. Limited attention has been paid to the persistent influence of teachers' perceived AI teaching stress on teaching anxiety and the potential buffering role of school support in this process. Methods Drawing on two-wave longitudinal tracking data, this study analyzed 553 valid primary and secondary school teachers at T1 and 348 matched teachers at T2 to examine the persistent impact of perceived AI teaching stress on teaching anxiety, as well as the buffering effect of school support. Results Teachers' stress and anxiety levels at T2 were both higher than those at T1, indicating an upward trend for both in the early stage of reform. Stress exerted a stable and significant positive effect on anxiety: stress at T1 significantly predicted anxiety at T1 (β ranging from 0.382 to 0.428, p < 0.001), and stress at T2 also significantly predicted anxiety at T2 (β ranging from 0.361 to 0.406, p < 0.001). Further analyses revealed clear cross-time continuity in both stress and anxiety, with stress at T1 significantly predicting stress at T2 (β ranging from 0.518 to 0.523, p = 0.001). Additionally, emotional support and training support significantly weakened the strength of the association between stress and anxiety. Discussion The findings indicate that teacher anxiety in the early phase of artificial intelligence curriculum reform is not a short-term psychological fluctuation, but a sustained psychological risk. This highlights the necessity for schools to identify such anxiety early and implement systematic interventions to address it. Keywords: artificial intelligence curriculum, longitudinal tracking, school support, teaching anxiety, teaching stress

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Secondary school. 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 adopted a mixed-methods design combining a two-wave longitudinal questionnaire survey with qualitative interviews. The T1 survey was conducted from October 1 to 15, 2025, corresponding to the initial stage after artificial intelligence curriculum reform entered school teaching practice. The T2 survey was conducted from December 1 to 15, 2025, corresponding to the adaptation stage after teachers had experienced a period of curriculum implementation. Both surveys measured teachers' perceived AI teaching stress and AI teaching anxiety in order to identify changes over time, synchronous associations, and cross-time continuity in the core psychological variables. School support was measured at T2, mainly to examine the level of support teachers actually perceived during the implementation of the reform and its buffering effect.

Limitations and uncertainty · source passage

This study still has several limitations. First, it adopted a two-wave longitudinal tracking design, which can reveal temporal associations and cross-time continuity among variables. However, because no experimental manipulation or random assignment was used, the conclusions are mainly limited to structural path relationships and longitudinal statistical associations. Future research could combine quasi-experimental designs, natural experimental designs, or more complete cross-lagged longitudinal designs to further examine the mechanisms linking artificial intelligence curriculum reform stress, school support, and teachers' anxiety.

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.

  • Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.

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. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. 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 unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. 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

Jiang Li. The first scene of artificial intelligence curriculum reform: longitudinal evidence on teachers' stress and multidimensional anxiety. Frontiers in Psychology (2026-06-15). https://doi.org/10.3389/fpsyg.2026.1835300

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.