Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Teachers’ perceptions of artificial intelligence in curriculum integration: opportunities, concerns, and professional development needs

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:

Abstract The growing presence of artificial intelligence (AI) in educational contexts has raised important questions regarding teachers’ readiness, perceptions, and concerns about its integration into curriculum and classroom practice. This qualitative study explores in-service teachers’ perspectives on artificial intelligence in relation to instructional processes, cognitive development, psychosocial implications, and professional learning needs. The study was conducted with 30 teachers working in public schools in a metropolitan city in Türkiye. Data were collected through a semi-structured interview form consisting of eight open-ended questions and analyzed using qualitative content analysis. Coding was carried out in three stages (open, axial, and selective coding), and inter-coder reliability was calculated at 87%. The findings reveal a balanced yet cautious stance toward AI. Teachers perceived AI as a tool that can enhance instructional efficiency, support the explanation of complex content, facilitate assessment processes, and enrich students’ cognitive development. At the same time, they expressed concerns about potential risks related to emotional development, reduced interpersonal interaction, and technological dependency. Across responses, participants emphasized the necessity of structured, practice-oriented professional development to support responsible and effective AI integration. Overall, the results suggest that teachers approach AI neither with uncritical enthusiasm nor outright resistance, but with cautious openness shaped by both pedagogical considerations and ethical concerns. These findings provide context-sensitive insights that may inform teacher education and curriculum planning efforts in emerging AI-integrated educational systems. Keywords: artificial intelligence in education, curriculum integration, professional development, qualitative research, teachers’ perceptions

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

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 was designed as a qualitative descriptive case study aiming to explore in-service teachers’ perceptions of artificial intelligence within the context of curriculum integration and child development. A qualitative approach was deemed appropriate because the research sought to understand teachers’ subjective meanings, concerns, and interpretations rather than to measure predetermined variables.

Limitations and uncertainty · source passage

This study should be interpreted in light of several limitations. First, the research was conducted with 30 in-service teachers from a single metropolitan area in Türkiye using convenience sampling. Although qualitative research does not aim for statistical generalization, the contextual nature of the sample limits the transferability of findings to different geographical regions or educational systems. Second, the predominance of female participants (86.6%) may have influenced the perspectives reported. While this distribution reflects the gender composition of certain teaching contexts, it may also shape attitudes toward technology integration. Future studies with more balanced gender representation would allow for deeper comparative analysis. Third, data were collected through written responses to a semi-structured interview form administered electronically. Although this method enabled reflective responses, it may have limited opportunities for probing and follow-up clarification that are possible in face-to-face interviews. Fourth, the study captures teachers’ perceptions at a particular moment in time. Given the rapidly evolving nature of artificial intelligence technologies, teachers’ attitudes and competencies may shift as AI becomes more embedded in everyday educational practice. Despite these limitations, the study provides context-sensitive qualitative insights into how teachers interpret the opportunities and challenges of AI integration at the curriculum level.

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. 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

Derya Kayıran; Mehtap Sönmez; Alperen Avcı; Rahma Yusuf Haji Mohamud. Teachers’ perceptions of artificial intelligence in curriculum integration: opportunities, concerns, and professional development needs. Frontiers in Artificial Intelligence (2026-04-07). https://doi.org/10.3389/frai.2026.1806165

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.