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An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification

arXiv · Jun 1

Researchers proposed a four-stage NLP framework combining schema-constrained LLM extraction, Sentence-BERT (SBERT) alignment with ESCO, an adjudication protocol, and a verification mechanism for curriculum-labor market alignment. The framework was instantiated for the ABET-accredited BSc Computer Science program at the United Arab Emirates University (UAEU), extracting 400 competency records from the study plan and aligning them with 30 job postings. The extractor achieved a Cohen's kappa of 0.79 on the skill slot and surfaced interpretable supply-demand gaps in general, transversal, algorithms, and software engineering skills, with a minimal gap in AI and data science. Why it matters: This framework provides a robust, NLP-driven method to identify crucial skill gaps in higher education curricula, directly supporting quality assurance and workforce development initiatives in the region.

Modeling Text as a Living Object

MBZUAI · Invalid Date

The InterText project, funded by the European Research Council, aims to advance NLP by developing a framework for modeling fine-grained relationships between texts. This approach enables tracing the origin and evolution of texts and ideas. Iryna Gurevych from the Technical University of Darmstadt presented the intertextual approach to NLP, covering data modeling, representation learning, and practical applications. Why it matters: This research could enable a new generation of AI applications for text work and critical reading, with potential applications in collaborative knowledge construction and document revision assistance.