做课程设计或教育数据分析的团队终于有了一个可复用的NLP对齐工具——从LLM提取到语义匹配再到缺口量化，全流程开源可验证，建议直接参考框架改造自己的课程体系。
本文提出一个四阶段NLP框架，用于解决课程与劳动力市场对齐中的隐性能力提取、缺乏共享分类法和可靠性度量问题。该框架结合了模式约束的LLM集成提取、Sentence-BERT与ESCO v1.2.1词汇的语义匹配、双模型裁决协议以及一致性验证机制。在阿联酋大学计算机科学专业的85门课程和30个岗位的实验中，提取器在技能槽上达到0.79的Cohen's kappa，100%模式合规和文档完整性。分析揭示了通用技能（25.0%）、算法理论（13.8%）和软件工程（12.2%）的供需缺口，而AI与数据科学缺口仅1.8%。该工作为高等教育质量保障提供了可量化的课程-市场对齐方法。
An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification
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.