A Retrieval-Augmented Generation Framework for Semantic Evaluation of Railway Education Curricula
Abstract
The European rail sector is facing two major challenges: the loss of expertise associated with an ageing workforce and the changing competence demands created by digitalisation, automation, and data-driven systems. These challenges increase the need for railway higher education programmes to remain aligned with evolving industry requirements. However, curriculum evaluation in this area is still largely manual, time consuming, resource intensive making systematic comparison across programmes difficult, especially where similar competencies are expressed through different terminology. This paper presents the development and preliminary testing of an AI-assisted curriculum evaluation tool for railway higher education. The tool transforms unstructured curriculum documents into comparable representations and combines rule-based comparison with semantically informed analysis. The study adopts a design-based research approach and reports a comparative case study of two European Master's-level rail-related programmes, focusing on learning outcomes and subject areas. The preliminary test showed that the tool could extract and structure curriculum information from both programmes and support comparison at both syntactic and semantic levels. It identified patterns of alignment, partial overlap, and apparent gaps, including cases where conceptually related content was expressed differently across programmes. The study shows the feasibility of using such a tool to support more systematic…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.