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.