用知识图谱和大模型生成可解释难度的多选题
Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models
- 用大模型从文档构建知识图谱，再基于图结构生成题目
- 通过9个难度信号融合得出可解释的难度分，与人工判断一致
- 适合教育AI系统、智能题库建设者使用
自动化多选题生成系统中，题目难度估计仍具挑战性。本文提出一种新方法，利用知识图谱（KG）与大语言模型（LLM）从输入文档生成带难度估计的多选题。首先用LLM将文档构建为知识图谱，随后以图中节点为关键信息，采样相关三元组或五元组（可附加额外三元组），并引导LLM生成题干。干扰项从图谱中选取。每道题计算九个难度信号，通过数据驱动方式融合为统一难度得分。实验表明，所生成题目质量高，难度估计可解释且符合人类感知。该方法通过结合结构化知识表示与大模型，提升了自动化出题能力。
原文摘要 · Abstract (English)
Generating multiple-choice questions (MCQs) with difficulty estimation remains challenging in automated MCQ-generation systems used in adaptive, AI-assisted education. This study proposes a novel methodology for generating MCQs with difficulty estimation from the input documents by utilizing knowledge graphs (KGs) and large language models (LLMs). Our approach uses an LLM to construct a KG from input documents, from which MCQs are then systematically generated. Each MCQ is generated by selecting a node from the KG as the key, sampling a related triple or quintuple -- optionally augmented with an extra triple -- and prompting an LLM to generate a corresponding stem from these graph components. Distractors are then selected from the KG. For each MCQ, nine difficulty signals are computed and combined into a unified difficulty score using a data-driven approach. Experimental results demonstrate that our method generates high-quality MCQs whose difficulty estimation is interpretable and aligns with human perceptions. Our approach improves automated MCQ generation by integrating structured knowledge representations with LLMs and a data-driven difficulty estimation model.
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