An Architectural Framework for Educational Knowledge Graphs (IEEE P2807.6): Ontology Design, Llm Integration, and Adaptive Learning Applications

The IEEE P2807.6 Education Knowledge Graph (EduKG) standard defines a semantic infrastructure to represent educational knowledge, resources, and pedagogy in a unified graph format. This paper expands on the core EduKG architecture, detailing its ontology design and key entities-Learning Points, Resource Items, and Pedagogical Rules-that collectively model the domain, content, and instructional strategies of learning systems. We further explore how EduKG can be integrated with advanced AI technologies, including large language models (LLMs) and retrieval-augmented generation (Graph-RAG) via embedding databases, to enable intelligent behavior such as semantic search, question answering, and dynamic content generation. These integrations position EduKG as a central component in next-generation smart education systems, wherein knowledge graphs work in concert with intelligent agents and adaptive instructional systems to deliver fully automated, personalized, and interactive learning experiences. By leveraging the standardized graph-structured representation and semantic reasoning capabilities of EduKG, such systems can achieve interoperability across platforms and support complex AI-driven tutoring and training scenarios. This work provides a comprehensive overview of the EduKG framework and highlights its role in empowering adaptive, cognitive, and collaborative learning solutions for the future of digital education.