Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus

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): Primary school, Secondary school, Higher education, Adult & professional learning. Verify the participant population and task before transferring the findings.

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

Abstract This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023–2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The “Artificial Intelligence Awareness Scale”, developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs. Keywords: artificial intelligence in education, teacher awareness, AI application in teaching, professional development, Northern Cyprus education

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Primary school; Secondary school; Higher education; 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

In the methods section of this study, the design of this study, the participants, the data collection tool, data collection procedures, and data analysis procedures were discussed.

Limitations and uncertainty · source passage

The integration of artificial intelligence (AI) in education is reshaping the roles of teachers, necessitating a paradigm shift in their professional responsibilities and the acceptance of new technologies. As AI tools become more prevalent, teachers are evolving from traditional information providers to facilitators of technology-enhanced learning experiences. This transformation is supported by research indicating that teachers must navigate and integrate AI effectively to optimize educational outcomes [42,43]. The acceptance of AI by teachers hinges on their understanding of its benefits and limitations, as well as institutional support for training and development [44]. For instance, AI can assist in creating personalized learning paths, automating administrative tasks, and providing data-driven insights that inform teaching strategies [46]. However, the success of AI in education depends not only on technological advancements but also on addressing ethical concerns, data privacy, and the potential impact on teacher employment [48,55]. Therefore, it is essential to foster a balanced approach that enhances the educational process while maintaining the irreplaceable human element in teaching [56]. Embracing this dual role of leveraging AI and preserving the core values of education can help teachers navigate the evolving landscape of modern classrooms effectively.

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. Pilot one teacher-led unit with short guided turns. The tutor asks for an explanation and offers a graduated hint before any worked solution. Use concrete examples and an equivalent task without AI. 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. Use an age-appropriate independent post-task and a delayed parallel task. Score explanation quality and transfer, not the number of agent messages or completion rate alone. 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

Ahmet Güneyli; Nazım Serkan Burgul; Sonay Dericioğlu; Nazan Cenkova; Sinem Becan; Şeyma Elif Şimşek; Hüseyin Güneralp. Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus. European Journal of Investigation in Health, Psychology and Education (2024-08-12). https://doi.org/10.3390/ejihpe14080156

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.