Abstract
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.
Abstract (translated)
集成大型语言模型（LLMs）和知识图（KGs）在颠覆智能教育方面具有巨大的潜力，但在实现个性化、互动性和可解释性方面仍然存在挑战。我们提出了FOKE，一种结合基础模型、知识图和提示工程的方法，以解决这些挑战。FOKE引入了三个关键创新：（1）用于表示结构化领域知识的分层知识森林；（2） comprehensive learner modeling 的多维度用户跟踪机制；（3）用于生成精确、定制化学习指导的交互式提示工程方案。我们在编程教育、作业评估和学习路径规划中展示了FOKE的应用，证明了其有效性和实用性。此外，我们还实现了Scholar Hero，一个基于FOKE的现实生活中实例。我们的研究突出了将基础模型、知识图和提示工程集成到智能教育实践中，以颠覆教育传统，最终为全球学习者带来利益的潜力。FOKE提供了一种理性和统一的方法，利用最先进的人工智能技术为个性化、互动性和可解释性教育服务，为这个关键领域的研究和开发铺平道路。
URL
https://arxiv.org/abs/2405.03734
