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EdFitter: a hybrid course recommendation system using large language models

human-computer-interaction
artificial-intelligence
data-mining-and-machine-learning
natural-language-and-speech
neural-networks

Abstract

Educational institutions face increasing pressure to adapt their curricula to the rapidly evolving demands of the job market, and recommender systems can be important tools to guide students in selecting courses that align with their career goals. We present EdFitter, a novel hybrid course recommendation system that integrates large language models (LLMs) for semantic skill extraction with robust ranking algorithms. By integrating LLM-generated embeddings with ranking algorithms, EdFitter bridges the gap between job descriptions and academic courses. We evaluate the system with an extensive case study with live potential users, demonstrating high relevance evaluation scores and high precision in skill identification, particularly for market-oriented fields. Results show that EdFitter is a valuable tool for both students and educators that can help align academia with industry, and we identify potential applications and potential directions for future research.
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