Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Teachers’ AI Belief Profiles and Their Associations with AI Use, Pedagogical Applications, and Barriers: A Person-Centered Analysis of TALIS 2024

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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

Teachers’ beliefs about the utility and risks of artificial intelligence (AI) may shape AI-related practice, yet these beliefs are often treated as a single attitude and practice as simple use. This study used a person-centered approach to identify configurations of AI utility and risk beliefs and examine their associations with AI use, pedagogical applications among users, and barriers among non-users. Data were drawn from 40,680 lower-secondary teachers across 49 education systems in TALIS 2024. Following fixed-alignment assessment of approximate measurement invariance, latent profile analysis, R3STEP, and posterior-probability-weighted comparisons were conducted. Four profiles emerged: Indifferent (6.13%), Skeptical (21.41%), Measured Endorsement (56.29%), and Risk-Aware Strong Endorsement (16.18%). AI-related professional learning was most consistently associated with profile membership. Reported AI use ranged from 12.8% in the Indifferent Profile to 76.3% in the Risk-Aware Strong Endorsement Profile, which also showed the highest rates across all eight pedagogical applications. Among non-users, pedagogical reservations were most prevalent in the Indifferent and Skeptical Profiles, knowledge-and-skills constraints in the Measured Endorsement Profile, and both knowledge-and-skills and resource constraints in the Risk-Aware Strong Endorsement Profile. These findings show that distinct belief configurations correspond to meaningful heterogeneity in AI-related practice and support profile-sensitive professional learning and institutional support.

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Age / stage not established. 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 used cross-sectional teacher-level data from the TALIS 2024 sample at level 2 of the International Standard Classification of Education (ISCED; OECD, 2025).

Limitations and uncertainty · source passage

How teachers’ AI practice is represented is subject to a corresponding limitation. Whether or not AI is used marks only the first threshold of practice. Teachers who use AI may do so for preparatory tasks, such as working with instructional content and designing lessons. They may also use it for tasks that enter the instructional process directly, such as assessment, feedback, the analysis of learning data, and adaptive support. These two kinds of application place different demands on teachers’ professional judgment (Celik et al., 2022; Hamilton et al., 2016). Teachers who do not use AI likewise face barriers of different kinds, some reflecting constraints in the conditions for implementation and others reservations grounded in pedagogical judgment (Selwyn, 2003; Wyatt, 2003). Reducing practice to use or non-use obscures both the functional differences among users and the heterogeneity of the constraints that non-users face.

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.

  • 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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Jie Fang; Zhijie Jin. Teachers’ AI Belief Profiles and Their Associations with AI Use, Pedagogical Applications, and Barriers: A Person-Centered Analysis of TALIS 2024. Behavioral Sciences (2026-09-28). https://doi.org/10.3390/bs16101762

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.