Use of artificial intelligence technologies in the process of building individual educational trajectories for higher education students

Abstract

The relevance of the study is driven by the rapid integration of artificial intelligence (AI) tools into higher education and the growing demand for personalized learning that reflects students' individual educational needs, professional interests, and competency development. At the same time, the use of AI for designing individual educational trajectories remains fragmented and is often limited to separate digital services, reducing its effectiveness within the university educational environment. The purpose of the article is to investigate the integration of AI tools into the process of developing individual educational trajectories for university students, identify the opportunities and limitations of their application, and propose an approach to their comprehensive integration into the academic support system. The study employed methods of system analysis, comparison, generalization, structural and functional analysis, modelling, and scientific abstraction, enabling a comprehensive examination of the organizational, methodological, technological, and ethical aspects of AI implementation in higher education. Contemporary approaches to the development of individual educational trajectories were examined, and the role of AI in learning personalization, academic advising, educational content adaptation, and decision support was identified. It was found that the effectiveness of AI largely depends on the degree of integration of university digital information systems, the completeness of educational data, and the possibility of their comprehensive analysis. The major organizational, methodological, technological, and ethical challenges were identified, including fragmented digital infrastructure, insufficient interoperability of educational data, algorithmic bias, limited transparency of AI-generated recommendations, and the necessity of preserving the leading role of academic advisors in educational decision-making. An approach based on an integrated student digital profile, an AI-driven analytical decision-support module, and expert validation of recommendations by an academic advisor was developed. It was substantiated that the proposed approach can improve the validity of individual educational trajectories, enhance learning personalization, and support more coordinated use of digital educational technologies in higher education. Future research should focus on experimental validation of the proposed approach across different types of higher education institutions, assessment of its impact on students' academic performance, and further development of explainable AI models to support academic advising.