The article presents a methodology for assessing the alignment between the content of educational programs and labor market requirements using intelligent text analysis tools. It addresses the issue of mismatch between university-acquired competencies and the actual needs of employers, especially in the context of rapid digitalization and economic transformation. The study substantiates the need to move from manual expert procedures to automated monitoring based on natural language processing models and ontological modeling. The proposed decision support system integrates the RuBERT model, the ESCO ontology, and the RCA metric, enabling the identification of gaps between curricula and job postings, data visualization, and the formulation of recommendations for curriculum adjustments. A practical case is presented, applying the methodology to a training program in the field of information security. The results demonstrate high accuracy in detecting mismatches and confirm the potential of using the system in the design and adaptation of educational programs. The scientific novelty lies in the comprehensive approach to competency analysis, combining linguistic and ontological methods with economic metrics. The methodology can be scaled to other industries and levels of education.
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Kozhevnikov Ilya Sergeevich
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