Use of artificial intelligence for personalization of the learning process in the context of mass digital education

Abstract

The transformation of modern education toward digital formats and the expansion of mass online learning have led to an increase in learners studying in digital environments, greater heterogeneity in their educational needs, and limited opportunities for individualized pedagogical support. Under these conditions, there is an increasing demand for tools that can adapt educational content, learning pace, and instructional formats to individual learner characteristics. The article aims to theoretically substantiate and generalize the potential of artificial intelligence technologies to personalize the learning process in the context of mass digital education, and to determine their impact on the effectiveness of educational platforms and the quality of learning outcomes. Methods. The study employs methods of systematic analysis, synthesis of scientific sources, comparative analysis of approaches to personalized learning, and structural-functional analysis of models for applying machine learning algorithms, adaptive systems, and learning analytics in digital educational environments. Results. The findings indicate that the use of artificial intelligence enables the formation of individualized learning pathways through automated analysis of learning data, prediction of academic performance, and adaptation of content according to learners’ prior knowledge and learning styles. It is demonstrated that implementing intelligent recommender systems, chatbots, and adaptive platforms increases learner motivation, reduces learning losses, and optimizes pedagogical resources. At the same time, risks related to ethical issues, data protection, and dependence on algorithmic decision-making are identified. Conclusions. It is concluded that artificial intelligence serves as an effective tool for personalizing the learning process in mass digital education, provided that technological solutions are integrated with pedagogical appropriateness and regulatory frameworks. Further research should focus on developing criteria for evaluating the effectiveness of intelligent educational systems and designing models for the responsible use of artificial intelligence in education.