AI-Driven Adaptive Learning for Higher Education

Yao, Yao and González-Vélez, Horacio (2026) AI-Driven Adaptive Learning for Higher Education. In: Multi-Agent Systems. EUMAS 2025. Lecture Notes in Computer Science (16259). Springer, Cham, Bucharest, Romania, pp. 226-235. ISBN 978-303222819-2

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Abstract

Adaptive learning refers to approaches that dynamically tailor content, pacing, and feedback to an individual learner’s progress, preferences, and prior knowledge. Large Language Models (LLMs) and Artificial Intelligence (AI) agents are transforming the landscape of higher education by enabling new forms of personalized, interactive, and adaptive learning. This paper argues for the intentional integration of AI agents powered by LLM into higher education to support adaptive learning grounded in the established pedagogical framework (Bloom’s Taxonomy). We propose that such an AI system can serve not merely as automated content providers but as intelligent, dialogic partners that guide learners across cognitive levels and disciplines through real-time feedback, personalized support, and collaborative knowledge construction. Drawing from educational theory and a practical use case developed by the authors, we demonstrate how knowledge-driven Human-AI interaction can be applied to diverse learning tasks to enhance learner engagement, cognitive development, and instructional scalability. This paper advocates for a future where AI agents augment rather than replace instruction, enabling meaningful, personalized, and cognitively rich learning experiences in higher education.

Item Type:	Book Section
Uncontrolled Keywords:	Adaptive Learning; Agent-based System; Human-AI Interaction; Large Language Models
Subjects:	L Education > LB Theory and practice of education > LB2300 Higher Education L Education > LF Individual institutions (Europe) L Education > Critical pedagogy Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence > Generative artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence > Generative artificial intelligence
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	25 Sep 2026 11:52
Last Modified:	25 Sep 2026 11:52
URI:	https://norma.ncirl.ie/id/eprint/9942

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