Artificial Intelligence-Powered Personalization in E-Learning for Higher Education
Keywords:
personalized learning, adaptive learning, recommender system, intelligent tutoring, learning analytics, fairness, explainability, OULADAbstract
AI-driven personalization tailors digital learning to each student, promising to improve engagement, performance, and retention. This paper surveys methods for AI-based personalization in higher education, identifies open challenges, and demonstrates an experimental study using the Open University Learning Analytics Dataset (OULAD). We review recommender systems, intelligent tutoring and adaptive engines, machine learning approaches (including sequential models and reinforcement learning), and NLP feedback tools. We also discuss learning analytics dashboards for at-risk student prediction, and ethics issues of fairness, transparency, and privacy. To illustrate an empirical approach, we apply three personalization strategies (rule-based, collaborative filtering, and a deep RL learner) on OULAD to adapt course content. Our mixed-effects analysis and interpretability evaluation show modest gains in completion and grades for personalized conditions, with careful inspection of subgroup fairness. Key contributions include a conceptual framework linking AI techniques to learning outcomes, a comparison of multiple personalization methods on real student data, and guidelines for ethical, explainable AI in education. The results underscore both the promise of personalization (consistent with prior findings) and the need to address bias and transparency.
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