Szczegóły publikacji
Opis bibliograficzny
Automated curriculum analysis using Large Language Models and knowledge graphs / Paulina Gacek, Weronika T. ADRIAN // Intelligenza Artificiale ; ISSN 1724-8035. — 2025 — vol. 19 iss. 2, s. 116–126. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-08-01
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162874 |
|---|---|
| Data dodania do BaDAP | 2025-09-24 |
| DOI | 10.1177/17248035251360196 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Intelligenza Artificiale |
Abstract
A well-structured curriculum is fundamental for providing students with a coherent and meaningful educational journey. However, distributed authorship, informal rules for writing syllabi, and the constant need for updates make curriculum development a highly challenging task. This paper introduces a novel framework that automates the analysis of existing curricula by detecting areas of inconsistency. It utilizes a Large Language Model to extract core concepts and prerequisite relationships directly from unstructured text in course syllabi. To ensure correctness and uniqueness, the extracted entities are linked to Wikidata, a collaborative and general-purpose knowledge graph. Subsequently, a curriculum knowledge graph is constructed based on the relationships between courses and educational concepts, laying the foundation for automated symbolic analysis. We demonstrate the effectiveness of our approach through experiments on the curriculum of the ‘Computer Science and Intelligent Systems program offered at AGH University of Krakow. The results are promising, as the tool provides actionable insights on how to improve the curriculum and avoid the most common mistakes.
