Recent advances in large language models (LLMs) have shown promise for scalable educational applications, but their use in dialog-based tutoring systems remains challenging due to the need for effective pedagogical strategies and the high costs associated with expert-curated datasets. Our study explores the use of smaller, more affordable LLMs for one-on-one tutoring in the context of solving reading comprehension problems. We developed a synthetic tutoring dialog dataset, evaluated by human teachers, and fine-tuned a smaller LLM using this dataset. Furthermore, we conducted an interactive experiment comparing the performance of the fine-tuned model with a larger model in real-world tutoring scenarios. Our results show that the fine-tuned model performs on par with the larger model but at a lower cost, demonstrating a viable, cost-effective approach for implementing LLM-based tutoring systems in educational settings.

本研究解决了对话式辅导系统中大型语言模型（LLMs）缺乏有效教学策略和高昂数据集成本的问题。我们提出了一种合成辅导对话数据集，并对小型LLM进行了微调，结果表明该模型在实际辅导场景中性能与大型模型相当，但成本显著降低，为教育环境中实施LLM辅导系统提供了可行且具有成本效益的方法。

开发一个辅导对话数据集以优化大型语言模型在教育中的应用