Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Development and validation of a scale on the difficulties of AI technology integration in teaching for primary and secondary school teachers

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

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

The lack of a comprehensive, theoretically grounded measurement instrument has constrained the systematic diagnosis of multidimensional barriers faced by K−12 teachers in integrating artificial intelligence (AI) into instruction. To address this gap, this study developed and validated a multidimensional scale based on the Cognitive–Emotional–Volitional–Behavioral (CEVB) heuristic framework. Through semi-structured interviews, expert consultation, and pilot testing, an initial 20-item pool was formed and administered to 546 teachers from central and western Anhui Province, China. Psychometric evaluation encompassed item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability testing, and validity and measurement invariance assessments. Item analysis confirmed satisfactory discrimination and differentiating power. EFA extracted a clear four-factor structure—cognitive understanding difficulties, affective attitudinal difficulties, conative motivational difficulties, and behavioral practice difficulties—accounting for 66.20% of the total variance. CFA supported both a first-order four-factor model (χ2/df = 2.014, CFI = 0.944, RMSEA = 0.061, SRMR = 0.056) and a higher-order model (χ2/df = 2.162, CFI = 0.935, RMSEA = 0.065, SRMR = 0.066). The scale demonstrated strong internal consistency (Cronbach's α = 0.915, McDonald's ω = 0.917). Convergent and discriminant validity were satisfactory, and strict measurement invariance was established across gender and school levels (primary, middle, and high school). Criterion-related validity was supported by significant positive correlations between scale dimensions and teacher AI literacy dimensions. Furthermore, structural equation modeling confirmed a significant sequential mediation pathway (F1 → F2 → F3 → F4), empirically validating the theoretical cascading process from cognitive deficits to affective resistance, then to motivational decline, and ultimately to behavioral impediments. This study provides a psychometrically robust, theory-driven diagnostic tool that enables phased and targeted interventions, moving beyond one-size-fits-all approaches to effectively support teachers' AI-integrated teaching.

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Secondary school; 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 study employed a combination of stratified random sampling and convenience sampling to survey primary and secondary school teachers in western Anhui Province from March to May 2026. To ensure sample representativeness, the sampling strategy comprehensively considered variations in region (urban, county-seat, rural), school level (elementary, middle, high), and subject area. Data were collected through an online survey using the “Questionnaire Star” platform. During the survey process, this study strictly adhered to ethical guidelines: an informed consent section was placed on the first page of the questionnaire, clearly stating the research purpose, the principles of anonymity and confidentiality, and the right to withdraw at any time without penalty. Teachers could proceed to answer the questions only after voluntarily providing consent. The questionnaire link and QR code were distributed via snowball sampling through school WeChat work groups.

Limitations and uncertainty · source passage

First, limitations regarding sample representativeness. The sample was primarily drawn from the central and western regions of Anhui Province in China and did not cover the more developed eastern areas or other cultural backgrounds. Although the sample demonstrated good representativeness for this region, objective disparities in digital infrastructure and educational resources exist across regions. Future research should expand the sampling scope to conduct cross-regional and cross-cultural measurement invariance tests and establish normative data, thereby examining the scale's applicability across broader geographic areas and diverse cultural contexts.

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.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

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

Shoujun Qiao; Yong Nie; Fuhe Niu; Zhenfeng He; Longlong Chen. Development and validation of a scale on the difficulties of AI technology integration in teaching for primary and secondary school teachers. Frontiers in Psychology (2026-08-04). https://doi.org/10.3389/fpsyg.2026.1902647

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.