.png){kind=icon}
.png){kind=icon}
Return
Generating Vicarious Dialogue for Online Learning Using Knowledge Graph-Based Retrieval-Augmented Generation
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-10156-3_12.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Dialogue-style learning materials are superior to lecture-style learning materials in many aspects. However, creating dialogue-style learning materials places a significant burden on teachers. In this paper, we propose a generative AI system that applies Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) for creating dialogue-style learning materials. The initial evaluation suggested that the KG-RAG approach has the potential to generate consistent and educationally appropriate dialogues, even when high-quality existing teaching materials are not available. These findings indicate that KG-RAG may offer a promising direction for producing dialogue-style materials in resource-scarce contexts, potentially reducing instructor workload while maintaining pedagogical value.
Keywords:
Educational Technology
Learning Material Generation
Vicarious Learning
Teacher Supp ort
Retrieval-Augmented
Generation
Journal
C
IF:
0
Papers:
18
Citations:
0