Enhancing Educational QA Systems: Integrating Knowledge Graphs and Large Language Models for Context-Aware Learning
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This study explores the integration of Knowledge Graphs (KG) and Large Language Models (LLMs) to design a question-answering (QA) system in the field of education. The proposed method involves constructing a KG using LLMs, retrieving contextual prompts from high-quality learning resources, and enhancing these prompts to generate accurate answers to real questions related to major educational concepts.
The technical framework outlined in this paper, along with the analysis of results, contributes to the advancement of LLM applications in educational technology. The findings provide a foundation for developing intelligent, context-aware educational systems that leverage structured knowledge to support learning and enhance educational outcomes.
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