Bridging the Education–Employment Gap: Linking Labour Market Needs with University Programmes Through AI-Assisted Insights and Micro-Credentials

This study explores how artificial intelligence (AI)-driven labour market analytics can support the alignment of university curricula with emerging skill demands and inform the development of targeted micro-credentials. A mixed-methods approach was applied, combining AI-assisted analysis of approximately 30,000 online job advertisements in Latvia with ESCO-based skill mapping, curriculum analysis, and expert interviews. The study develops and validates a three-layer analytical framework integrating labour market demand, professional standards, and programme learning outcomes. Three occupations—Personnel Specialist, Finance Manager, and Organisation Manager—were analysed at Riga Technical University as proof-of-concept cases. The findings demonstrate that strict one-to-one ESCO matching overestimates curriculum gaps because labour market and educational actors often describe competencies at different levels of abstraction. Composite matching significantly improves alignment estimates by identifying functionally equivalent competencies embedded across curricula. Nevertheless, the analysis reveals a persistent under-representation of digital competencies across all programmes, confirmed by industry experts. Interviews further identify a “pedagogical transfer gap”, where formally acquired competencies are insufficiently applied in practice, and highlight employer support for high-quality micro-credentials focused on technical upskilling. The study contributes an AI-assisted curriculum-monitoring framework that combines large-scale skill extraction, semantic alignment, and stakeholder validation, offering universities a practical tool for evidence-based curriculum renewal and lifelong learning development.