XCRS: an Explainable Course Recommendation System for Information Technology Careers Powered by LLMs
Abstract
The growing number of online resources on information technology has left many learners feeling overwhelmed by the large number of career options and the paths to achieve them. This abundance of choices highlights the need for personalized career guidance and clear course recommendations to help learners focus on their specific goals. Existing recommendation systems fail to provide transparency and clear explanations for their suggestions. To bridge this gap, we present XCRS: Explainable Course Recommendation System, which recommends both career roles and associated courses in information technology with explainability at its core. XCRS utilizes large language model embeddings from Google, OpenAI, MistralAI, VoyageAI, and Cohere to deliver personalized recommendations tailored to users’ knowledge, past preferences, and future learning interests. Our contributions are two-fold: i) a pipeline to construct an explainable recommendation system for career pathways in information technology, ii) a replication package that includes the implementation, a public dataset of information technology courses, and the design for empirical evaluation. Our evaluation suggests that the overall system has been perceived as useful by the intended users, while there is no statistically significant difference in the performance of the large language models used.
Paper Type
Full Paper
DOI
10.62036/ISD.2025.133
XCRS: an Explainable Course Recommendation System for Information Technology Careers Powered by LLMs
The growing number of online resources on information technology has left many learners feeling overwhelmed by the large number of career options and the paths to achieve them. This abundance of choices highlights the need for personalized career guidance and clear course recommendations to help learners focus on their specific goals. Existing recommendation systems fail to provide transparency and clear explanations for their suggestions. To bridge this gap, we present XCRS: Explainable Course Recommendation System, which recommends both career roles and associated courses in information technology with explainability at its core. XCRS utilizes large language model embeddings from Google, OpenAI, MistralAI, VoyageAI, and Cohere to deliver personalized recommendations tailored to users’ knowledge, past preferences, and future learning interests. Our contributions are two-fold: i) a pipeline to construct an explainable recommendation system for career pathways in information technology, ii) a replication package that includes the implementation, a public dataset of information technology courses, and the design for empirical evaluation. Our evaluation suggests that the overall system has been perceived as useful by the intended users, while there is no statistically significant difference in the performance of the large language models used.
Recommended Citation
Horasanli, M.Y., Aydemir, F.B. & Yilmaz, H.B. (2025). XCRS: an Explainable Course Recommendation System for Information Technology Careers Powered by LLMsIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.133