Modern educational systems increasingly incorporate machine learning technologies to deliver personalized learning experiences and improve educational outcomes. Dialogue-based intelligent tutoring systems, reinforcement learning models, predictive learning analytics, large language models, and multimodal affective systems are widely used to adapt instruction, provide feedback, and monitor learner engagement. These approaches enable scalable tutoring, automated content generation, adaptive sequencing of learning materials, and early intervention for at-risk learners. Empirical studies report measurable improvements in learner engagement, academic performance, and instructional efficiency when AI-driven personalization is implemented. However, significant challenges remain, including data governance and privacy concerns, algorithmic bias, integration with existing learning management systems, and the need for robust cross-context evaluation. This paper reviews major machine learning model families used in personalized learning systems, highlights their benefits and limitations, and summarizes architectural and implementation practices emerging from recent research and deployments.
Keywords
Personalized learning, Machine learning in education, Intelligent tutoring systems, Large language models, Reinforcement learning, Learning analytics, Multimodal affective computing, Adaptive education
Conclusion
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How to Cite This Paper
Aaman Pansare, Saud Kokate, Dipali Bhusari (2026). Machine Learning Models for Personalized Learning in Modern Educational Systems. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
