@inproceedings{fu-etal-2025-conquer,
title = "{C}on{Q}uer: A Framework for Concept-Based Quiz Generation",
author = "Fu, Yicheng and
Wang, Zikui and
Yang, Liuxin and
Huo, Meiqing and
Dai, Zhongdongming",
editor = "Ebrahimi, Abteen and
Haider, Samar and
Liu, Emmy and
Haider, Sammar and
Leonor Pacheco, Maria and
Wein, Shira",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
month = apr,
year = "2025",
address = "Albuquerque, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-srw.9/",
doi = "10.18653/v1/2025.naacl-srw.9",
pages = "92--104",
ISBN = "979-8-89176-192-6",
abstract = "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."
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%0 Conference Proceedings
%T ConQuer: A Framework for Concept-Based Quiz Generation
%A Fu, Yicheng
%A Wang, Zikui
%A Yang, Liuxin
%A Huo, Meiqing
%A Dai, Zhongdongming
%Y Ebrahimi, Abteen
%Y Haider, Samar
%Y Liu, Emmy
%Y Haider, Sammar
%Y Leonor Pacheco, Maria
%Y Wein, Shira
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-192-6
%F fu-etal-2025-conquer
%X 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.
%R 10.18653/v1/2025.naacl-srw.9
%U https://aclanthology.org/2025.naacl-srw.9/
%U https://doi.org/10.18653/v1/2025.naacl-srw.9
%P 92-104
Markdown (Informal)
[ConQuer: A Framework for Concept-Based Quiz Generation](https://aclanthology.org/2025.naacl-srw.9/) (Fu et al., NAACL 2025)
ACL
- Yicheng Fu, Zikui Wang, Liuxin Yang, Meiqing Huo, and Zhongdongming Dai. 2025. ConQuer: A Framework for Concept-Based Quiz Generation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 92–104, Albuquerque, USA. Association for Computational Linguistics.