Date
Publisher
arXiv
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.
Study at a glance
- Question: How can AI models and structured knowledge be combined to provide personalized, interactive, and clear learning guidance?
- Setting and data: Not stated in the abstract
- Method: System design and demonstration of an educational AI framework and software application
Why it matters
Integrating large language models and knowledge graphs holds great promise for intelligent education, but challenges remain in achieving personalization, interactivity, and explainability.
Key findings
- In programming education, homework assessment, and learning path planning, a unified AI framework demonstrated practical and effective personalized learning support.
- Combining detailed learner profiles with structured subject knowledge enables AI to generate precise, customized learning guidance for students.
- A real-world tool demonstrated that integrating language models with organized subject knowledge delivers interactive, personalized, and explainable educational services.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who age?
Why use AI?
Study design
