This systematic literature review explores the construction and application of knowledge graphs (KGs) in education. The review focuses on five key domains: Adaptive and Personalised Learning, Curriculum Design and Planning, Concept Mapping and Visualization, Semantic Search and Questioning Answering, and Miscellaneous Applications. The study aims to provide a comprehensive overview of KGs in education, analyze state-of-the-art methodologies, and identify research gaps and limitations. KGs are structured representations of knowledge that have gained significant attention in education due to their potential to enhance personalized learning, curriculum design, concept mapping, and educational content recommendation systems. The review highlights the specific functionalities of KGs, knowledge extraction techniques, knowledge base characteristics, resource requirements, evaluation criteria, and limitations in each domain. It also discusses the importance of domain-specific KGs in education, emphasizing their ability to provide in-depth insights and context within specialized subject areas. The review identifies several key challenges in the application of KGs in education, including limited discussion on knowledge extraction techniques, lack of standardization, limited interoperability, sparse and incomplete data, and scalability challenges. These challenges hinder the effective integration and utilization of KGs in educational settings. The study also emphasizes the need for collaborative efforts to develop common ontologies, define data interchange standards, and promote best practices in data representation to address these issues. Overall, the review underscores the transformative potential of KGs in education, highlighting their ability to provide tailored, data-driven, and adaptive educational experiences. However, further research is needed to overcome the identified limitations and to fully realize the benefits of KGs in educational contexts.This systematic literature review explores the construction and application of knowledge graphs (KGs) in education. The review focuses on five key domains: Adaptive and Personalised Learning, Curriculum Design and Planning, Concept Mapping and Visualization, Semantic Search and Questioning Answering, and Miscellaneous Applications. The study aims to provide a comprehensive overview of KGs in education, analyze state-of-the-art methodologies, and identify research gaps and limitations. KGs are structured representations of knowledge that have gained significant attention in education due to their potential to enhance personalized learning, curriculum design, concept mapping, and educational content recommendation systems. The review highlights the specific functionalities of KGs, knowledge extraction techniques, knowledge base characteristics, resource requirements, evaluation criteria, and limitations in each domain. It also discusses the importance of domain-specific KGs in education, emphasizing their ability to provide in-depth insights and context within specialized subject areas. The review identifies several key challenges in the application of KGs in education, including limited discussion on knowledge extraction techniques, lack of standardization, limited interoperability, sparse and incomplete data, and scalability challenges. These challenges hinder the effective integration and utilization of KGs in educational settings. The study also emphasizes the need for collaborative efforts to develop common ontologies, define data interchange standards, and promote best practices in data representation to address these issues. Overall, the review underscores the transformative potential of KGs in education, highlighting their ability to provide tailored, data-driven, and adaptive educational experiences. However, further research is needed to overcome the identified limitations and to fully realize the benefits of KGs in educational contexts.