FOKE: A Personalized and Explainable Education Framework Integrating Foundation Models, Knowledge Graphs, and Prompt Engineering
Project Overview
The document presents FOKE, an innovative framework that integrates large language models (LLMs) and knowledge graphs to transform educational practices by enhancing personalization, interactivity, and interpretability in learning experiences. FOKE tackles key challenges in education through its components, including hierarchical knowledge representation, multi-dimensional user profiling, and structured prompt engineering. It demonstrates its effectiveness in various applications such as programming education, personalized learning path planning, and intelligent writing assessment. By leveraging generative AI, FOKE aims to create more engaging and tailored educational experiences, ultimately revolutionizing how students learn and interact with educational content. The findings suggest that such AI-driven approaches can significantly improve educational outcomes by making learning more accessible and customized to individual needs.
Key Applications
Personalized Learning and Feedback Systems
Context: Providing interactive programming tutorials and recommending personalized course sequences based on learner profiles. This includes generating prompts for programming tasks and suggesting learning paths to help learners navigate their educational journey.
Implementation: Utilizes AI technologies to generate personalized prompts and learning paths by analyzing learner profiles and course information. This includes automating feedback on writing assignments and providing insights based on predefined criteria.
Outcomes: Enhances understanding of programming concepts, improves problem-solving skills, aids in navigating course selections, and promotes writing skills through actionable feedback.
Challenges: Requires continuous updates to knowledge bases, accurate user profiling, dynamic course information, and may struggle with subjective aspects of writing that necessitate human interpretation.
Implementation Barriers
Technical
The complexity of integrating multiple AI technologies into existing educational systems
Proposed Solutions: Iterative development and pilot testing to refine system capabilities
Data
Limited and domain-specific data may not capture the diversity of learners
Proposed Solutions: Incorporating a broader range of educational data for comprehensive profiling
Evaluation
Lack of standardized evaluation methods for comparing different educational AI systems
Proposed Solutions: Collaboration with domain experts to establish effective evaluation criteria
Project Team
Silan Hu
Researcher
Xiaoning Wang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Silan Hu, Xiaoning Wang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai