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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.14662 |
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| _version_ | 1866908274435031040 |
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| author | Fu, Yicheng Wang, Zikui Yang, Liuxin Huo, Meiqing Dai, Zhongdongming |
| author_facet | Fu, Yicheng Wang, Zikui Yang, Liuxin Huo, Meiqing Dai, Zhongdongming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ConQuer: A Framework for Concept-Based Quiz Generation Fu, Yicheng Wang, Zikui Yang, Liuxin Huo, Meiqing Dai, Zhongdongming Computation and Language Artificial Intelligence 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. |
| title | ConQuer: A Framework for Concept-Based Quiz Generation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.14662 |