PERSONALIZED LEARNING SYSTEMS BASED ON ARTIFICIAL INTELLIGENCE: MODELS, METHODS AND APPLICATIONS.

Abstract

The article addresses the pressing issue of developing and implementing personalized learning systems based on artificial intelligence (AI) methods. The aim of the research is to identify mechanisms and models for adapting educational content to the individual characteristics of learners. The study analyzes existing architectures of intelligent tutoring systems, including models employing machine learning, neural networks, and reinforcement learning algorithms.

The research methodology combines theoretical analysis with experimental modeling: an architecture of a personalized learning system was designed, algorithms for clustering and dynamic content adaptation were developed, and modeling was conducted using student learning data. Experimental results demonstrated that the implementation of AI-driven personalization increases learner engagement, reduces time to mastery, and improves learning efficiency by 25–40% compared to traditional e-learning platforms.

The proposed model integrates a modular structure, intelligent analytics, and real-time feedback mechanisms to ensure individualized learning trajectories. This work has practical significance for e-learning systems, EdTech platforms, and the field of digital pedagogy