Ethical risks of AI-based adaptive e-learning systems and a conceptual model for their minimization in higher education - Hryshchuk, Nazar (ScienceRise: Pedagogical Education, 2026)
This article looks at ethical risks that arise when AI-based adaptive e-learning systems are used in higher education. These tools are usually discussed in terms of better individualization of learning. That is true to an extent, their use is still not neutral. Alongside clear benefits, less visible issues start to appear. Some are familiar, for example protection of personal data, algorithmic bias. Others are harder to pin down: a gradual shift in teacher autonomy, changes in how teachers and students interact, and a growing dependence on automated decisions. Taken together, these changes affect not only the technical side of education, but also how learning is organized. The discussion draws on international frameworks, including the UNESCO Recommendation on the Ethics of Artificial Intelligence and the General Data Protection Regulation (GDPR), as well as recent work in digital pedagogy. In this context, the notion of an “ethical risk of an adaptive e-learning system” is revisited and organized into four groups: risks related to protection of personal data and security, algorithmic bias, reduced autonomy of participants, and educational inequality. The study combines several approaches: theoretical analysis, comparison of international practices, and conceptual modeling. The resulting model is not meant to be universal. Rather, it is a framework that can be adjusted to a specific institutional context, it includes four stages: an ethical audit, pedagogical adjustment of algorithms and content, development of digital-ethical competence, and current monitoring with feedback. One difficulty, as it seems, is not the lack of tools but the way they are used. Technical, pedagogical, and legal approaches often remain separate. Because of this, outcomes depend heavily on context, including how national strategies for changes driven by digital technologies in education are interpreted in practice. The results may be useful for universities developing internal AI policies, designing courses in digital ethics, and building adaptive e-learning environments