Volume 10 • Issue 1 • PP: 01–05 • 2025
Development Knowledge Graphs for Intelligent Curriculum Design in Education with Artificial Intelligence
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Curriculum design is a critical aspect of education, requiring careful consideration of content relevance, student progression, and pedagogical coherence. In recent years, the use of Knowledge Graphs (KG) has gained attention for their ability to represent complex relationships between concepts in a structured format. This paper introduces KGCD (Knowledge Graph-based Curriculum Design), a novel approach to intelligent curriculum design that leverages knowledge graphs to model subject matter interdependencies, skill progression, and student learning paths. By incorporating AI-driven insights, KGCD offers educators a powerful tool for designing adaptive, personalized curricula that align with student needs and educational goals. The system provides real-time suggestions for curriculum adjustments, ensuring the inclusion of relevant content and logical sequencing of topics. Initial pilot studies demonstrate KGCD’s potential to improve curriculum coherence and student learning outcomes by providing data-driven support for curriculum development and revision.
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References
[1] B. Abu-Salih and S. Alotaibi, “A systematic literature review of knowledge graph construction and application in education,” Heliyon, vol. 10, no. 3, p. e25383, 2024.
[2] M. Aydın and H. Yurdugül, “Developing a curriculum framework of artificial intelligence teaching for gifted students,” Kastamonu Education Journal, vol. 32, no. 1, pp. 14–37, 2024.
[3] M. Dinesh, C. Arvind, S. S. Sreeja Mole, C. S. Subash Kumar, P. Chandra Sekar, K. Somasundaram, K. Srihari, S. Chandragandhi, and V. P. Sundramurthy, “An energy efficient architecture for furnace monitor and control in foundry based on industry 4.0 using IoT,” Scientific Programming, vol. 2022, p. 1128717, 2022.
[4] P. Chandra Sekar and H. Mangalam, “Third generation memetic optimization technique for energy efficient routing stability and load balancing in MANET,” Cluster Computing, vol. 22, no. Suppl. 5, pp. 11 941–11 948, 2019.
[5] Z. Chen, J. Shi, and H. Jin, “Research on the teaching evaluation system and path of diversified courses in blended teaching based on knowledge graph under the background of artificial intelligence,” Frontiers in Educational Research, vol. 7, no. 10, pp. 21–30, 2024.
[6] B. Cope, M. Kalantzis, C. Zhai, A. Krussel, D. Searsmith, D. Ferguson, R. Tapping, Y. Berrocal et al., “Maps of medical reason: Applying knowledge graphs and artificial intelligence in medical education and practice,” in Bioinformational Philosophy and PostdigitaKnowledge Ecologies, M. A. Peters, P. Jandri´c, and S. Hayes, Eds. Cham, Switzerland: Springer, 2022, pp. 133–159.
[7] D. H. Kim and R. Shakya, “A study on the curriculum design for K–12 AI education using knowledge graph,” European Journal of Science, Innovation and Technology, vol. 3, no. 4, pp. 515–531, 2023.
[8] R. U. Maheshwari, D. Jayasutha, I. V. Nair, R. Senthilraja, S. Thanappan, and S. Ramya, “Development of digital twin technology in hydraulics based on simulating and enhancing system performance,” Journal of Cybersecurity and Information Management, vol. 13, no. 2, pp. 50–65, 2024.
[9] B. Paulchamy, S. Chidambaram, J. Jana, and R. U. Maheshwari, “Diagnosis of retinal disease using retinal blood vessel extraction,” in International Conference on Mobile Computing and Sustainable Informatics: ICMCSI 2020, ser. EAI/Springer Innovations in Communication and Computing, J. S. Raj, Ed. Cham, Switzerland: Springer, 2021, pp. 343–359.
[10] B. Paulchamy, R. Uma Maheshwari, D. Sudarvizhi AP, R. Anandkumar AP, and G. Ravi, “Optimized feature selection techniques for classifying electrocorticography signals,” in Brain–Computer Interface: Using Deep Learning Applications, M. G. Sumithra, R. K. Dhanaraj, M. Milanova, B. Balusamy, and C. Venkatesan, Eds. Hoboken, NJ, USA: Wiley-Scrivener, 2023, pp. 255– 278.
[11] P. Sun and L. Gu, “Fuzzy knowledge graph system for artificial intelligence-based smart education,” Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2929– 2940, 2021.
[12] Q. Wen and S. Zhang, “Analysis of the current research status of intelligent education in china based on knowledge graph,” in Proc. 2024 6th International Conference on Computer Science and Technologies in Education (CSTE), 2024, pp. 56–61.
[13] L. Wu, F. Tu, Q. Liu, X. Ma, X. Jia, and Y. Gao, “A review of intelligent application of educational knowledge graph from the perspective of artificial intelligence,” in Proc. 2022 International Symposium on Educational Technology (ISET), 2022, pp. 195–199.
[14] Z. Yang, Y. Wang, J. Gan, H. Li, and N. Lei, “Design and research of intelligent question-answering (Q&A) system based on high school course knowledge graph,” Mobile Networks and Applications, vol. 26, no. 5, pp. 1884–1890, 2021.
[15] H. Yue, H. Lin, Y. Jin, H. Zhang, and K. Cai, “Opening knowledge graph model building of artificial intelligence curriculum,” International Journal of Emerging Technologies in Learning (iJET), vol. 17, no. 14, pp. 64–77, 2022.
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