AN INTEGRATED APPROACH TO ANALYZING COURSE STRUCTURE USING LLM AND PREREQUISITE GRAPHS
Keywords:
language models, artificial intelligence, analysis of educational materials, knowledge graphsAbstract
Educational materials in university courses exhibit a hierarchical structure, where the understanding of new concepts is based on previously introduced ones. During the creation, revision, or integration of materials from different sources, structural inconsistencies may arise, including incorrect ordering of concept introduction, conflicting definitions, isolated concepts, and cyclic dependencies, which reduce the coherence and consistency of the course. Detecting such inconsistencies requires simultaneous consideration of textual semantics and the structure of relationships between concepts.
This paper proposes an integrated method for analyzing the structure of educational materials, combining the use of large language models for extracting concepts, their definitions, introduction time, and prerequisite relationships with the construction of a temporal knowledge graph and subsequent graph-based algorithmic analysis. The method is implemented as a four-stage pipeline, including material structuring, LLM-based extraction, entity normalization, and graph integration, followed by structural analysis using transitive checking, semantic conflict detection, and importance ranking based on PageRank centrality. Additionally, a precision-oriented mode is employed, incorporating validation of results using a language model.
Experimental evaluation was conducted on MIT OpenCourseWare datasets using controlled injection of structural inconsistencies and three evaluation protocols. The proposed method demonstrates significantly higher detection performance (F1 up to 0.738) compared to baseline approaches, including single-prompt language model and TF-IDF. It is also shown that a substantial portion of formally identified false positives corresponds to real structural issues in the course.
The scientific contribution lies in integrating concept and relation extraction using large language models with temporal knowledge graph construction and algorithmic analysis of structural consistency in educational materials.
References
Abu-Salih B., Alotaibi S. A systematic literature review of knowledge graph construction and application in education. Heliyon. 2024. Vol. 10, no. 3. Article e25383. DOI: https://doi.org/10.1016/j.heliyon.2024.e25383
Li Y., Liang Y., Yang R., Qiu J., Zhang C., Zhang X. CourseKG: An educational knowledge graph based on course information for precision teaching. Applied Sciences. 2024. Vol. 14, no. 7. Article 2710. DOI: https://doi.org/10.3390/app14072710
Pan L., Li C., Li J., Tang J. Prerequisite relation learning for concepts in MOOCs. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver, Canada : Association for Computational Linguistics, 2017. P. 1447–1456. DOI: https://doi.org/10.18653/v1/P17-1133
AlRabah A., Kargupta P., Han J., Alawini A. Instructor-aligned knowledge graphs for personalized learning. arXiv preprint. 2026. arXiv:2602.17111. DOI: https://doi.org/10.48550/arXiv.2602.17111
Canal-Esteve M., Gutierrez Y. Educational material to knowledge graph conversion: A methodology to enhance digital education. Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024). Bangkok, Thailand : Association for Computational Linguistics, 2024. P. 85–91. DOI: https://doi.org/10.18653/v1/2024.kallm-1.9
Boylan J., Mangla S., Thorn D., Gholipour Ghalandari D., Ghaffari P., Hokamp C. KGValidator: A framework for automatic validation of knowledge graph construction. arXiv preprint. 2024. arXiv:2404.15923. DOI: https://doi.org/10.48550/arXiv.2404.15923
Bian H. LLM-empowered knowledge graph construction: A survey. arXiv preprint. 2025. arXiv:2510.20345. DOI: https://doi.org/10.48550/arXiv.2510.20345
