A Framework-Based Analysis of AI-Driven Personalized Learning Systems in Higher Education
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Abstract
The rise of AI-driven personalized learning systems represents a transformative development in higher education, reshaping how learners access content, engage with learning pathways, and construct academic understanding. Grounded in advances in machine learning, user modeling, and adaptive instructional design, these systems aim to deliver customized learning experiences aligned with individual cognitive profiles, performance patterns, and learner dispositions. This review provides a framework-based analysis of AI-driven personalized learning in universities by synthesizing perspectives from learning sciences, instructional design theory, data-driven personalization models, and human–AI interaction frameworks. Drawing on principles of constructivism, self-regulated learning theory, precision pedagogy, and adaptive learning architectures, the paper examines how AI systems tailor instructional content, feedback mechanisms, and assessment processes to support learner success. The analysis reveals that while personalized AI systems offer significant pedagogical benefits, including enhanced engagement, adaptive scaffolding, and data-informed teaching, they also introduce risks related to algorithmic bias, reduced learner autonomy, opaque personalization decisions, and infrastructural inequities. The review concludes by proposing a theoretically grounded, ethically responsible framework for implementing AI-driven personalized learning systems in higher education, emphasizing transparency, agency, equity, and pedagogical coherence as core pillars of sustainable integration.
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