Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: General and special education teachers’ readiness for artificial intelligence in classrooms: A structural equation modeling study of knowledge, attitudes, and practices in select UAE public and private schools

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): Adult & professional learning. Verify the participant population and task before transferring the findings.

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

As artificial intelligence (AI) reshapes global education systems, understanding educators’ readiness to integrate AI into classroom practices is essential. This study examines the knowledge, attitudes, and practices (KAP) of general and special education teachers in the United Arab Emirates (UAE) regarding AI in education. Drawing on the Concerns-Based Adoption Model (CBAM) and Universal Design for Learning (UDL), we used structural equation modeling (SEM) to assess the relationships among KAP domains, including the moderating effects of demographic factors such as teaching experience, academic role, and prior exposure to AI tools. Data were collected from 161 educators in selected public and private schools across four UAE emirates, with the majority representing private and urban school settings. The findings revealed that teachers’ attitudes significantly predicted AI-related classroom practices, whereas knowledge had a weaker, but positive association. Mediation analysis further showed that knowledge had a significant indirect effect on practice through attitudes, confirming the hypothesized KAP pathway. Moderation analyses highlighted the variability in AI engagement based on gender and academic position, suggesting differentiated readiness across the subgroups. This study contributes to global conversations on teacher preparedness by offering a model for assessing institutional and pedagogical readiness for AI integration in urban school contexts. Implications for professional development, inclusive curriculum design, and educational technology policy are discussed, with relevance to digitally transforming educational systems in comparable settings.

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Adult & professional learning. 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 quantitative cross-sectional survey design to examine teachers’ knowledge, attitudes, and practices toward AI in education, along with the moderating effects of demographic factors.

Limitations and uncertainty · source passage

While this study provides valuable insights into UAE K–12 teachers’ readiness for AI integration, several limitations should be acknowledged. First, the cross-sectional design precludes causal inferences among the core constructs. Although structural equation modeling identifies directional relationships, longitudinal or intervention-based studies are needed to assess how teacher readiness evolves over time in response to training or policy shifts.

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.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

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 bounded job-relevant simulation or professional-development task with an expert supervisor. Require a traceable rationale and verification before any real-world action. 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. Assess independent performance on an unfamiliar simulation, retention after a delay, error severity and total review time. Do not equate a faster AI-assisted response with durable professional competence. 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

Mohammad Fteiha; Mohammad Al-Rashaida; Mohammed Ghazal. General and special education teachers’ readiness for artificial intelligence in classrooms: A structural equation modeling study of knowledge, attitudes, and practices in select UAE public and private schools. PLOS One (2025-09-12). https://doi.org/10.1371/journal.pone.0331941

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.