ARCHITECTURAL DESIGN OF AN INTELLIGENT EDUCATIONAL PLATFORM BASED ON ADAPTIVE AI ALGORITHMS

Abstract

This article presents a conceptual architecture for an intelligent educational platform in higher education. The study focuses on integrating adaptive algorithms that automatically adjust instructional content according to students’ proficiency levels. The proposed platform employs a modular structure, which supports personalized learning through data analytics and machine learning techniques. A four-tier architecture was developed, including the Presentation, Service, Logic, and Data layers. To evaluate student knowledge and adjust instructional materials, the system incorporates models such as Bayesian Knowledge Tracing and Graph Convolutional Networks. Simulation-based testing was conducted to verify the logical consistency of the platform in the absence of real users. The results indicate that the architecture performs effectively and shows potential for integration into contemporary Learning Management Systems. Overall, this study highlights the role of adaptive technologies in enhancing personalized education and provides a foundation for further research in intelligent learning platforms.