Abstract
The article presents a conceptual architecture of an intelligent system for predicting the career trajectories of students in technical disciplines, based on the integration of educational analytics and labor market requirements analysis. A comprehensive analysis was conducted to substantiate the proposed architecture: (1) employer requirements based on data from the HeadHunter.kz platform using web scraping methods in Python; (2) students academic performance and learning preferences using logistic regression, decision trees, and principal component analysis (PCA). It was found that most vacancies in the IT sector require 1–3 years of work experience, while the key in-demand competencies include communication skills, teamwork, and analytical thinking. It is shown that practice-oriented activities of students (internships, projects, additional courses) are among the most significant factors of successful career development. A conceptual dual-loop architecture of an intelligent forecasting system is proposed, comprising an institutional loop (analyzing the alignment of educational programs with labor market requirements) and an individual loop (providing personalized recommendations to students). The architecture consists of three database layers and three specialized analyzers: an educational program alignment analyzer, an academic performance and student preferences analyzer, and a curriculum-to-student profile matching analyzer that correlates program courses with individual student data. The proposed architecture is designed to automate the identification of gaps between educational programs and labor market demands, as well as to generate evidence-based recommendations for curriculum revision and course selection by students. It is expected that the implementation of the proposed architecture will improve the quality of specialist training, increase graduate employability, and enhance the adaptability of higher technical education to the requirements of the digital economy.