"This project explores the use of Large Language Models (LLMs) to automate the construction of pedagogy-aware Educational Knowledge Graphs (EduKGs), addressing the challenges of manual development. While LLMs support self-guided learning, their effectiveness depends on access to structured, syllabus-aligned educational data. Educational Knowledge Graphs offer this structure, but incorporating pedagogical metadata such as learning outcomes and learner states remains a complex and underexplored task. To overcome this, the project presents a hybrid framework that combines ontological modeling with LLM-driven automation. The framework enables the generation of EduKGs that capture both educational content and pedagogical context. A data pipeline is established to allow natural language querying through Graph Retrieval Augmented Generation (GraphRAG), where the LLM interacts with the EduKG to retrieve and generate responses. The system also integrates learner-specific data into the EduKG to support personalized learning experiences and provide better visibility into alignment with learning goals. The proposed system, EduGraphAI, was evaluated through both qualitative and quantitative methods. Expert interviews assessed the system's relevance, design accuracy, and adaptability in educational settings. Quantitative evaluation benchmarked the extraction of Learning Outcomes and Concepts against a gold standard dataset using Precision, Recall, and F1 Score. Learning Outcome extraction achieved an F1 Score of 82.35 percent. Concept extraction reached 80.00 percent Precision, 87.50 percent Recall, and an F1 Score of 83.58 percent. These results demonstrate the system's effectiveness in automating the creation of educational knowledge graphs while maintaining pedagogical integrity."