用外部知识生成更高质量的教育测验，提升学习效果。
ConQuer: A Framework for Concept-Based Quiz Generation
- 基于概念与外部知识生成测验，确保内容准确
- 评分提升4.8%，对战基线胜率77.52%
- 适合教育科技、AI助教研发者使用
测验在教育中至关重要，能强化学生对核心概念的理解并促进自主探索。然而，编制高质量测验需深厚专业知识，难度较大。尽管大语言模型（LLMs）显著提升了测验生成效率，但其生成内容的质量和对学生的学习影响仍存疑。为此，我们提出ConQuer——一种基于概念的测验生成框架，利用外部知识源增强生成质量。通过多维度评估体系，以LLM为评判标准进行评测。实验表明，该框架生成的测验在评分上比基线提升4.8%，在成对比较中胜率高达77.52%。消融实验证明了框架各组件的有效性。代码已开源：https://github.com/sofyc/ConQuer。
原文摘要 · Abstract (English)
Quizzes play a crucial role in education by reinforcing students' understanding of key concepts and encouraging self-directed exploration. However, compiling high-quality quizzes can be challenging and require deep expertise and insight into specific subject matter. Although LLMs have greatly enhanced the efficiency of quiz generation, concerns remain regarding the quality of these AI-generated quizzes and their educational impact on students. To address these issues, we introduce ConQuer, a concept-based quiz generation framework that leverages external knowledge sources. We employ comprehensive evaluation dimensions to assess the quality of the generated quizzes, using LLMs as judges. Our experiment results demonstrate a 4.8% improvement in evaluation scores and a 77.52% win rate in pairwise comparisons against baseline quiz sets. Ablation studies further underscore the effectiveness of each component in our framework. Code available at https://github.com/sofyc/ConQuer.
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