UniSkill: A Dataset for Matching University Curricula to Professional Competencies

2026-03-03

AI Summary

This study addresses the scarcity of publicly available course–occupational skill alignment data in educational skill recommendation systems. We present the first fine-grained dataset that aligns graduate-level courses with skills from the European Skills/Competences, Qualifications and Occupations (ESCO) framework—specifically for Systems Analysts and Management and Organizational Analysts. The dataset integrates human-annotated labels and synthetically generated data at both course title and sentence levels, accompanied by a standardized annotation guideline. Leveraging this resource, we train BERT-based language models to perform bidirectional semantic retrieval between courses and skills. Baseline models achieve an F1 score of 87% on the annotated subset, demonstrating the feasibility of the task and filling a critical gap in skill-mapping data on the educational side.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Evaluation and Analysis

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad attention, deficiencies in the instructed skills side remain a challenge. In this work, we address the scarcity of publicly available datasets by releasing both manually annotated and synthetic datasets of skills from the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and university course pairs and publishing corresponding annotation guidelines. Specifically, we match graduate-level university courses with skills from the Systems Analysts and Management and Organization Analyst ESCO occupation groups at two granularities: course title with a skill, and course sentence with a skill. We train language models on this dataset to serve as a baseline for retrieval and recommendation systems for course-to-skill and skill-to-course matching. We evaluate the models on a portion of the annotated data. Our BERT model achieves 87% F1-score, showing that course and skill matching is a feasible task.

Problem

• skill extraction
• university curriculum
• professional competencies
• dataset scarcity
• course-skill matching

Innovation

• skill-course matching
• ESCO taxonomy
• curriculum alignment
• BERT-based retrieval
• annotated educational dataset