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

TL;DR
FOKE is a novel educational framework that combines foundation models, knowledge graphs, and prompt engineering to enable personalized, interactive, and explainable learning experiences, demonstrated through programming education and learning path planning.
Contribution
The paper introduces a hierarchical knowledge forest, multi-dimensional user profiling, and an interactive prompt scheme, advancing personalized and explainable AI-driven education.
Findings
Effective in programming education and homework assessment
Enhances personalized learning through comprehensive learner modeling
Demonstrates practical application with Scholar Hero system
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…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Semantic Web and Ontologies
