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 three key innovations: (1) a hierarchical knowledge forest for structured domain knowledge representation; (2) a multi-dimensional user profiling mechanism for comprehensive learner modeling; and (3) an interactive prompt engineering scheme for generating precise and tailored learning guidance. We showcase FOKE's application in programming education, homework assessment, and learning path planning, demonstrating its effectiveness and practicality. Additionally, we implement Scholar Hero, a real-world instantiation of FOKE. Our research highlights the potential of integrating foundation models, knowledge graphs, and prompt engineering to revolutionize intelligent education practices, ultimately benefiting learners worldwide. FOKE provides a principled and unified approach to harnessing cutting-edge AI technologies for personalized, interactive, and explainable educational services, paving the way for further research and development in this critical direction.

翻译：将大语言模型（LLMs）与知识图谱（KGs）相结合，为革新智能教育带来了巨大潜力，但在实现个性化、交互性和可解释性方面仍存在挑战。我们提出FOKE（知识教育森林框架），该框架协同整合基础模型、知识图谱与提示工程技术以应对这些挑战。FOKE包含三项关键创新：（1）用于结构化领域知识表征的分层知识森林；（2）用于全面学习者建模的多维用户画像机制；（3）用于生成精准定制学习指导的交互式提示工程方案。我们展示了FOKE在编程教育、作业评估与学习路径规划中的实际应用，验证了其有效性与实用性。此外，我们实现了FOKE的真实世界实例化系统Scholar Hero。本研究凸显了融合基础模型、知识图谱与提示工程以革新智能教育实践的潜力，最终惠及全球学习者。FOKE为利用前沿AI技术打造个性化、交互式且可解释的教育服务提供了原则性统一方法，为该关键方向的后续研究与发展铺平了道路。