Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Ethical use of artificial intelligence in education: proposed ethical competency framework for 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): Adult & professional learning. Verify the participant population and task before transferring the findings.

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

Abstract Introduction The rapid integration of Artificial Intelligence (AI) in education is transforming teaching, learning, and professional development through personalized instruction, adaptive learning, and data-driven educational practices. However, in Pakistan and Saudi Arabia, many teachers still lack the ethical awareness and practical competencies required for the responsible use of AI technologies. Challenges related to data privacy, algorithmic bias, transparency, accountability, and equitable access continue to hinder effective AI adoption in teacher education and classroom practice. This study proposes a contextualized ethical competency framework to support the responsible and effective integration of AI among teachers in both countries. Methods An exploratory sequential mixed-methods design was employed, comprising a systematic literature review and a pilot implementation study involving 30 teachers from diverse educational levels and institutional contexts. The framework was developed through a qualitative synthesis of international AI ethics frameworks, responsible AI principles, and teacher competency literature. It was subsequently evaluated using quantitative measures and qualitative feedback collected from participating teachers. Results The findings provide preliminary evidence that teachers perceived the proposed framework positively in terms of effectiveness (M = 3.73), usability (M = 3.63), and ethical compliance (M = 3.83). Qualitative findings further indicated that the framework enhanced ethical awareness, responsible AI use, personalized instructional practices, and AI-supported learning design. Participants also identified several implementation challenges, including limited prior AI knowledge, insufficient technical support, and the need for continuous professional development. Discussion and conclusion This study contributes to the growing body of research on ethical AI integration in education by introducing a localized framework that combines ethical governance with practical AI integration competencies for teachers in Pakistan and Saudi Arabia. The findings underscore the importance of sustained teacher training, institutional readiness, ethical governance, and supportive policies to facilitate responsible AI adoption in education. Future research should conduct large-scale longitudinal studies to further validate the framework and evaluate its scalability, effectiveness, and long-term educational impact across diverse educational settings. Keywords: AI integration, artificial intelligence, education technology, ethical competencies, teachers

What kind of evidence is this?

Intelligent tutoring & adaptive learning · 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

To evaluate the applicability and preliminary effectiveness of the proposed framework, a pilot study was conducted involving 30 teachers from different educational levels and institutional contexts in Pakistan and Saudi Arabia. The pilot study adopted a purposive sampling strategy, where participants were selected based on their familiarity with digital educational technologies and willingness to participate in AI-supported teaching activities. Purposive sampling is commonly used in exploratory educational research to obtain contextually relevant insights from participants with practical experience related to the study topic (Cohen, 1988).

Limitations and uncertainty · source passage

This study has several limitations that should be acknowledged. First, the pilot study involved a relatively small sample of teachers from Pakistan and Saudi Arabia, which limits the generalizability of the findings across broader educational contexts and subject areas such as science, mathematics, humanities, and social sciences. Therefore, the findings should be interpreted as preliminary and exploratory rather than conclusive.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.

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

Ibrahim Yaussef Alyoussef; Nisar Ahmed Dahri; Khadijah Amru Alhashmi; Waleed Mugahed Al-Rahmi; Paras Muzna; Fida Hussain Dahri. Ethical use of artificial intelligence in education: proposed ethical competency framework for teachers. Frontiers in Artificial Intelligence (2026-08-03). https://doi.org/10.3389/frai.2026.1833043

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.