The rapid evolution of digital technologies has created significant misalignment between academic curricula and labor market demands for digital skills. This study presents the development and validation of an intelligent curriculum alignment model that systematically analyzes university curricula, identifies digital competencies, and maps them to labor market requirements. The research employed a hybrid artificial intelligence approach combining web scraping, Natural Language Processing (NLP), and Decision Tree machine learning algorithms to create a comprehensive solution. The model achieved an accuracy of 88% in classifying digital skills relevance, demonstrating strong performance with high precision (0.99) for low-frequency skills and exceptional recall (0.99) for high-frequency skills. The study utilized data from 46,514 job postings across 27 occupations, resulting in a skillset database of 9,077 unique digital skills categorized by market demand. The intelligent model generates three key outputs: a Curriculum Analysis Report, a Digital Skills Index (DSI) scoring curriculum alignment on a 0-100 scale, and a Prescriptive Analytics Report providing data-driven recommendations for curriculum improvement. This research addresses critical gaps in educational responsiveness to technological change and provides a scalable framework for continuous curriculum optimization in the digital era.

Keywords: Digital skills, curriculum alignment, artificial intelligence, natural language processing, higher education, workforce development, educational technology, machine learning.