FOKE: A Personalized and Explainable Education Framework Integrating Foundation Models, Knowledge Graphs, and Prompt Engineering

Integrating large language models (LLMs) and knowledge graphs (KGs) holds great promise for revolutionizing intelligent education, but challenges remain in achieving personalization, interactivity, and explainability. We propose FOKE, a Forest Of Knowledge and Education framework that synergizes foundation models, knowledge graphs, and prompt engineering to address these challenges. FOKE introduces key innovations: a hierarchical knowledge forest for structured domain knowledge representation, a multi-dimensional user profiling mechanism for comprehensive learner modeling, and an interactive prompt engineering scheme for generating precise and tailored learning guidance. We implement Scholar Hero, a real-world instantiation of FOKE, showcasing its capabilities in delivering personalized, interactive, and explainable educational services. Our research highlights the potential of integrating foundation models, knowledge graphs, and prompt engineering to revolutionize intelligent education practices, ultimately benefiting learners worldwide.