Generative Artificial Intelligence (GenAI), particularly large language models (LLMs) like ChatGPT, has the potential to scale personalized feedback and reduce the workload of teaching and instruction. However, GenAI faces challenges in educational applications, such as generating outputs with hallucinations, lacking explainability in reasoning, and producing lengthy responses. Additionally, GenAI often fails to meet education-related standards, such as curriculum requirements and learner-related data, making it less context-sensitive in authentic courses. Knowledge Graphs (KGs), characterized by their structured representation of entities, relations, and attributes, can provide consistent answers and hierarchical reasoning about information clusters. The integration of KGs with LLMs shows promise in linking graduate requirements and knowledge points, supporting interdisciplinary knowledge theme design in STEAM projects. Despite these potentials, there is a lack of comprehensive reviews on how KGs combined with LLMs can enrich learning experiences and empower educators in the dynamic landscape of modern education. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this systematic review examines selected studies to highlight specific KG functionalities, the role of LLMs in knowledge extraction, data resources, LLMs used, and evaluation methods. The review contributes to three key areas: applications of KGs combined with LLMs in education, a data workflow from the data source to evaluation in the application context, and opportunities for KGs combined with LLMs to support lifelong learning and reduce educational inequality utilizing open resources.