APPLICATION OF ARTIFICIAL INTELLIGENCE FOR THE INDIVIDUALIZATION OF EDUCATIONAL TRAJECTORIES IN VOCATIONAL EDUCATION

Abstract

The article examines the role of artificial intelligence in vocational education as an innovative tool for implementing personalized learning. It analyzes current concepts and models of adaptive content as well as the experience of using AI technologies in various educational institutions.

Key directions for implementing adaptive algorithms, chatbots, and learning analytics to create individualized educational trajectories for vocational learners are identified. Special attention is paid to domestic and international examples of application demonstrating the effectiveness of personalized learning.

Particular emphasis is placed on the main challenges and barriers to applying artificial intelligence in technical and vocational education–from technical infrastructure to ethical considerations–and on outlining prospects for further research in developing hybrid learning models and regulatory frameworks for the use of AI in vocational education.

The purpose of the work is to investigate the feasibility of applying artificial intelligence in vocational education to shape individualized educational trajectories for learners.

Methodology. The study employs methods of analyzing scientific sources and publications, comparative analysis of educational platform capabilities, synthesis of applied experience in implementing AI technologies in vocational institutions, and expert evaluation of the potential of personalized models.

The scientific novelty. The research consolidates AI tools that influence the personalization of educational trajectories within the vocational education system. A structural implementation model is proposed, and the barriers and prospects for using AI to individualize learning in Ukrainian vocational institutions are identified.

Conclusions. The study confirms that applying artificial intelligence in technical and vocational education opens new opportunities for creating flexible and adaptive educational trajectories tailored to individual learner characteristics and proficiency levels. Analysis of adaptive content concepts and the experience of chatbots and analytical platforms shows that integrating such solutions enhances learner motivation, improves learning efficiency, and enables instructors to respond more rapidly to student needs. At the same time, it has been found that AI projects require not only the modernization of technical infrastructure and staff training but also the development of clear regulatory mechanisms to protect data and ensure the ethical use of technology. The proposed structural model provides practical guidance for Ukrainian vocational institutions and lays a foundation for further research on hybrid learning systems and AI governance standards in the educational environment.