An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification
2026-06-01 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created a new step-by-step process to better extract skills and competencies from educational course data and job postings. They used advanced language models guided by specific schemas, checked the results for accuracy, and matched the skills to a controlled vocabulary. Testing this on a computer science curriculum and job ads in the UAE showed their method could reliably find gaps between what students learn and what jobs require. This helps understand where education might need improvement to meet labor market needs.
information extractionschema-constrained promptinglanguage modelsSentence-BERTESCO vocabularyCohen's kappacurriculum-labor market alignmentcompetency formalismABET accreditationhigher-education quality assurance
Authors
Sherzod Turaev, Mary John, Mamoun Awad, Nazar Zaki, Khaled Shuaib
Abstract
Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness. To address these limitations, this paper proposes a four-stage NLP framework that combines (i) schema-constrained prompting of a two-model frontier-LLM ensemble against a JSON Schema-enforced seven-slot competency formalism, (ii) Sentence-BERT (SBERT) alignment of the extracted records against an eleven-domain ESCO v1.2.1 controlled vocabulary, (iii) a two-tier adjudication protocol that resolves inter-model disagreements, and (iv) a verification mechanism that combines per-slot Cohen's kappa, schema conformance, and document-level completeness audits. The framework is instantiated for a critical application in higher-education quality assurance, namely curriculum-labor market alignment for the ABET-accredited BSc Computer Science program at the United Arab Emirates University. The pipeline extracts 400 competency records from the 85-course 2025-2026 study plan and aligns them, under a five-scope analysis ranging from the computing core to a probability-weighted student trajectory, with 30 job postings (483 requirement clauses) at an SBERT cosine threshold of 0.50. The extractor achieves Cohen's kappa of 0.79 on the skill slot, with 100% schema conformance and 100% document-level completeness. The alignment surfaces interpretable supply-demand gaps of 25.0% in general and transversal skills, 13.8% in algorithms and computational theory, and 12.2% in software engineering and project management, with a near-zero 1.8% gap in artificial intelligence and data science despite 38.6% supply coverage.