THEORETICAL MODELS UNDERPINNING AI-ASSISTED PERSONALIZED LEARNING IN HIGHER EDUCATION
Keywords:
Artificial intelligence in education, personalized learning, higher education, theoretical modelsAbstract
AI is widely used in higher education to tailor learning. Recent systematic evaluations suggest that AI-based systems that dynamically alter material, tempo, and feedback to individual learners increase academic achievement, engagement, and perceived learning quality. However, technological feasibility rather than explicit pedagogical philosophy sometimes guides system design, risking algorithmic judgments misaligning with educational goals. This essay analyzes the theoretical frameworks behind AI-assisted individualized learning in higher education. Based on recent empirical and review studies on AI-supported personalization, learning sciences, and AI in education, the paper uses constructivism, socio-constructivism, self-regulated learning, connectivism, learning-analytics frameworks, and the “three paradigms” of AI in education to explain AI-driven personalization. Based on this, an integrative model integrates learner-level educational theories with AI learner-modelling and adaption processes and system-level institutional and ethical frameworks. The study claims that theoretically based AI-assisted customization increases learner agency, data transparency, and instructor control in increasingly automated learning settings.
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