Core Concept Identification in Educational Resources via Knowledge Graphs and Large Language Models

Abstract The growing demand for online education raises the question of which learning resources should be included in online programs to ensure students achieve their desired learning outcomes. By automatically identifying the core concepts in educational materials, teachers can select coherent and relevant resources for their courses. This work explores the use of Large Language Models (LLMs) to identify core concepts in educational resources. We propose three different pipelines for building knowledge graphs from lecture transcripts using LLMs and ontologies such as DBpedia. These knowledge graphs are then utilized to determine the central concepts (nodes) within the educational resources. Results show that LLM-constructed knowledge graphs when guided by ontologies, achieve state-of-the-art performance in core concept identification.