• Anastasiia O. Ivashchenko, St. Petersburg Federal Research Center of the Russian Academy of Sciences (St. Petersburg, Russia)

This article systematizes modern methods of intelligent career guidance and proposes a unified computational model of recommendations that integrates psychometric data, digital footprints, academic indicators, and textual descriptions of professions. The relevance of the study is driven by the transition from isolated diagnostic techniques to comprehensive, data-driven career support systems capable of accounting for heterogeneous user information and the dynamics of professional trajectories. The aim of the work is to enhance the quality and interpretability of career guidance recommendations by analyzing existing approaches and developing a mathematically rigorous model that integrates three key tasks: reconstructing professional interests, multimodal matching between user preferences and occupational profiles, and ranking possible career trajectories. The article presents an overview of studies published between 2020 and 2025, demonstrating improvements in the accuracy and robustness of career guidance methods achieved through ensemble algorithms, multimodal deep learning architectures, and LLM-based conversational systems. The analysis shows that combining psychometric profiles, digital activity, and textual descriptions of professions substantially increases the quality of recommendations in both interest reconstruction and career selection tasks. The scientific novelty of the work lies in proposing a unified theoretical and computational framework for intelligent career guidance, which provides a common mathematical structure for diverse existing approaches and enables viewing them as special cases of a general model. The proposed framework includes a formal representation of user and profession data, a RIASEC profile reconstruction function, a parameterized multimodal matching mechanism, and a joint optimization objective that simultaneously trains all components. This formalization establishes a foundation for developing interpretable, reproducible, and scalable career guidance systems.

career guidance, career choice, artificial intelligence, Holland Codes (RIASEC), multimodal data, digital footprint, intelligent systems, psychometrics

2026-06-05