In the domain of Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated promise in text-generation tasks. However, their educational applications, particularly for domain-specific queries, remain underexplored. This study investigates LLMs' capabilities in educational scenarios, focusing on concept graph recovery and question-answering (QA). We assess LLMs' zero-shot performance in creating domain-specific concept graphs and introduce TutorQA, a new expert-verified NLP-focused benchmark for scientific graph reasoning and QA. TutorQA consists of five tasks with 500 QA pairs. To tackle TutorQA queries, we present CGLLM, a pipeline integrating concept graphs with LLMs for answering diverse questions. Our results indicate that LLMs' zero-shot concept graph recovery is competitive with supervised methods, showing an average 3% F1 score improvement. In TutorQA tasks, LLMs achieve up to 26% F1 score enhancement. Moreover, human evaluation and analysis show that CGLLM generates answers with more fine-grained concepts.

本研究探索了大型语言模型在教育应用中的能力，重点关注概念图恢复和问题回答。研究通过引入TutorQA，一个新的专家验证的自然语言处理基准，评估了大型语言模型在领域特定的概念图和问题回答中的性能，证明其零-shot概念图恢复具有竞争力，且CGLLM生成的答案具有更精细的概念。

利用大型语言模型进行NLP教育中的概念图恢复和问题回答