Analysis of methods for supporting personalization in IT education

Abstract

The increasing complexity of digital learning environments and the growing demand for individualized educational experiences underscore the importance of developing effective methods for personalizing practical tasks. The purpose of this study is to analyze existing approaches to the personalization of practical tasks in IT education and to identify their advantages, limitations, and potential for integration into digital learning ecosystems. The main objectives include the identification of technological solutions that enable automated generation and assessment of personalized practical tasks, and their effectiveness for supporting adaptive and scalable learning processes. The research methodology is based on a comparative analysis of scholarly sources, existing educational platforms, and practical implementations of personalized tasks. Special attention is given to the development of conceptual frameworks that enable personalization not only at the level of task complexity but also in terms of content variability, learning pathways, and assessment mechanisms. The review identifies the most effective strategies for achieving scalable personalization in digital education. The analysis also reveals the limitations of current solutions. The critical limitations are: insufficient support for comprehensive practical task parametrization, high technical barriers for instructors, incomplete learning management system integration, security vulnerabilities in local deployment scenarios, and limited automation in assessment processes. These constraints highlight the need for more sophisticated, user-friendly solutions. The findings provide a foundation for developing integrated information technologies that combine personalization methods with adaptive learning and artificial intelligence tools, supporting the evolution of digital learning ecosystems toward more effective, scalable, and integrity-focused educational approaches. The scientific novelty of the research lies in the systematic comparison of personalization methods and the identification of criteria for their effectiveness in IT education. The practical significance is determined by the possibility of integrating the proposed approaches into digital platforms, thereby improving the quality of training, optimizing the learning process, and ensuring the flexibility required by modern educational ecosystems.