Personalized machine learning

Abstract

The article presents the concept of an adaptive learning platform that uses machine learning and artificial intelligence methods for personalization of the educational process. The authors examine the problem of the need for continuous educational development in the modern context of rapidly changing technologies. The paper analyzes existing educational platforms with AI elements and identifies their limitations: fragmentation of approaches, difficulties in structural representation of didactic units, and problems with integrating pedagogical activities. The authors propose architectural principles for the LADAP platform, including five interconnected components, with the Knowledge Module being central, implementing Retrieval-Augmented Generation technology. The concept aims to create a unified approach to designing educational systems for personalized learning that can be flexibly adapted to students' cultural preferences and effectively solve a complex of educational tasks.