AI-enabled curriculum analytics framework for aligning higher education with labour market skill demand

Universities face growing pressure to adapt curricula to rapidly changing skill demands shaped by digitalisation, artificial intelligence, and labour-market transformation. Yet curriculum review often remains periodic, internally driven, and only indirectly connected to large-scale evidence on emerging labour-market needs. This study develops the Curriculum Analytics Framework (CAF), an AI-enabled methodology for comparing the skills supplied by university curricula with the skills demanded in the labour market. The CAF integrates curriculum text analysis, online job vacancy data, data analytics, natural language processing, large language model-supported skill extraction, and the ESCO skills taxonomy to construct a common representation of curriculum skill supply and labour-market skill demand. The framework is demonstrated using a database of 43,522 job advertisements scraped from five major online vacancy platforms in Latvia and a bachelor-level study programme at the Stockholm School of Economics in Riga. It operationalises curriculum–labour-market alignment through three complementary metrics: the Weighted Mean Gap, which captures differences in skill intensity; Spearman rank correlation, which assesses similarity in skill prioritisation; and importance-weighted coverage, which evaluates whether high-demand labour-market skills are represented in the curriculum. The paper contributes to applied AI and skills analytics by integrating four elements into a transparent and reproducible curriculum-labour market alignment framework: an open ESCO-based occupational and skills taxonomy, LLM-assisted extraction and mapping of vacancy and curriculum texts, graduate-pathway-based occupational benchmarks, and three complementary alignment measures capturing skill intensity, coverage and rank-order prioritisation. The framework transforms heterogeneous education and labour-market texts into comparable skill profiles and provides interpretable evidence for curriculum review, labour-market intelligence and workforce development.