DEVELOPMENT OF AN INTELLIGENT RECOMMENDER SYSTEM FOR SUPPORTING PROSPECTIVE STUDENTS DURING THE UNIVERSITY ADMISSION CAMPAIGN

Abstract

The article examines the transformation of university admission processes under the influence of digitalization, growing competition, and changes in applicants’ behavior. It focuses on the shift from traditional administrative procedures to personalized digital services integrated into the strategic development of higher education institutions. Special attention is paid to intelligent systems based on machine learning and data analysis, which support applicants by generating individualized recommendations and facilitating informed decision-making. The study explores the role of predictive models, the structure of digital admission ecosystems, and the challenges related to data management, algorithmic transparency, and ethical requirements. The findings demonstrate that AI-driven recommendation tools enhance the effectiveness of communication between universities and prospective students, improve the quality of educational choice, and strengthen strategic management within admission campaigns.