Ethical Risks of Generative AI in Education: Challenges, Implications, and a Responsible Use Framework - Samyo Ranjan Jagdev; Chinmay Giri; G. Subhasmita Prusty; Girija Nandan Das (International Journal of Education Sciences, 2026)
The rapid diffusion of generative artificial intelligence (AI) technologies in educational settings is reshaping how teaching, learning, and assessment are designed and enacted. Large language models and related generative systems offer powerful capabilities for content creation, personalized feedback, and instructional support, promising gains in efficiency and learner engagement. However, their growing use also introduces a complex set of ethical risks that challenge foundational educational values such as integrity, equity, transparency, and trust. This paper critically examines the ethical implications of generative AI in education through a comprehensive synthesis of existing literature and policy discussions. The analysis identifies six interrelated ethical risk dimensions that consistently emerge across educational contexts: academic integrity and authorship, cognitive dependency, bias, and inequality, privacy, and data protection, transparency, and accountability, and assessment fairness. Rather than treating these concerns in isolation, the study demonstrates how they interact and reinforce one another, amplifying potential negative impacts on learning quality, student outcomes, and institutional credibility. In particular, the opacity of generative AI systems and uneven access to AI tools raise significant concerns regarding fairness, explainability, and the legitimacy of assessment practices. Building on this analysis, the paper proposes a four-pillar ethical framework for the responsible integration of generative AI in education, grounded in integrity, equity, transparency, and privacy. The framework translates ethical principles into actionable institutional and pedagogical strategies, including assessment redesign, AI disclosure policies, bias auditing, AI literacy initiatives, and compliance with data protection regulations. By aligning ethical considerations with practical implementation, the framework offers a holistic approach that moves beyond purely technical solutions. The paper contributes to ongoing debates on AI ethics in education by consolidating fragmented ethical concerns into a unified structure and providing guidance for educators, administrators, and policymakers. It argues that the educational value of generative AI ultimately depends not on technological capability alone, but on the ethical frameworks that govern its use, ensuring innovation supports meaningful, equitable, and trustworthy learning experiences.
Keywords: Academic Integrity; ai ethics; and fairness; bias; education technology; generative artificial intelligence; responsible AI