Ethical Integration of Artificial Intelligence in Teacher Education: Opportunities, Risks and Policy Implications
Jadaun, D.
NIEPA, New Delhi, India
Abstract
The rapid integration of Artificial Intelligence (AI) into educational systems is transforming teaching–learning processes, assessment practices, and institutional decision-making. In teacher education, AI-powered tools such as adaptive learning systems, automated feedback mechanisms, generative AI platforms, and predictive analytics offer significant opportunities for personalization, efficiency, and professional development. However, these innovations raise critical ethical concerns related to data privacy, algorithmic bias, academic integrity, equity, transparency, and professional autonomy. This conceptual paper examines the ethical dimensions of AI integration in teacher education, with specific focus on fairness in assessment, inclusivity, data governance, and the evolving role of teachers. Drawing upon contemporary literature in educational technology, digital ethics, and policy frameworks, the paper proposes an ethical integration framework grounded in principles of transparency, accountability, human oversight, and contextual sensitivity. The analysis highlights that while AI can enhance pedagogical effectiveness and support differentiated instruction, unregulated implementation risks reinforcing social inequities and compromising professional ethics. The paper argues for structured ethical guidelines, curriculum-level digital ethics training for pre-service teachers, and policy alignment to ensure responsible AI adoption. The study contributes to ongoing discourse on modern innovations and ethical practices in education by offering a balanced, research-informed framework for integrating AI responsibly within teacher education programs.
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Jadaun, D. (2026) ‘Ethical Integration of Artificial Intelligence in Teacher Education: Opportunities, Risks and Policy Implications’, Eurasian Journal of Education and Psychology, 1(1), ISSN 3121-4363, pp. 77. https://doi.org/10.68283/ejep.v1.i1.a09