DESIGNING ARTIFICIAL INTELLIGENCE-BASED PERSONALIZED LEARNING IN HIGHER EDUCATION: A CONCEPTUAL MODEL AND PEDAGOGICAL-ETHICAL CONDITIONS
Keywords:
Keywords: artificial intelligence; personalized learning; adaptive learning; learning analytics; learner model; explainable artificial intelligence; academic integrity.Abstract
Abstract. This article examines the theoretical and methodological foundations
for designing artificial intelligence (AI)-based personalized learning in higher
education. The study does not report empirical findings; rather, it presents a
conceptual-analytical review grounded in peer-reviewed research and official
documents issued by UNESCO and the OECD. The analysis systematizes the potential
of AI for learner modelling, learning-outcome prediction, adaptive content selection,
individualized feedback, and teacher decision support. Based on the synthesis, a six-
component conceptual model is proposed, comprising data acquisition, a dynamic
learner profile, a pedagogical decision mechanism, adaptive content, continuous
feedback, and ethical-pedagogical governance. The model is founded on a balance
between algorithmic automation and meaningful human oversight. The article also
discusses data privacy, algorithmic bias, explainability, academic integrity, digital
inequality, and the risks of excessive dependence on AI. It argues that AI should be
implemented not as a substitute for teachers, but as a tool that supports evidence-
informed pedagogical judgement. The proposed model may provide a theoretical
foundation for the design of adaptive courses, intelligent tutoring systems, and learning
analytics platforms in higher education.
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