@inproceedings{xu-etal-2026-edubench,
title = "{E}du{B}ench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios",
author = "Xu, Bin and
Bai, Yu and
Sun, Huashan and
Lin, Yiguan and
Liu, Siming and
Liang, Xinyue and
Li, Yaolin and
Dong, Zhuangzhi and
Zhang, Jingren and
Deng, Yufan and
Zou, Xinyu and
Gao, Yang and
Huang, Heyan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.987/",
pages = "21615--21645",
ISBN = "979-8-89176-390-6",
abstract = "As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing the first diverse benchmark tailored for educational scenarios, incorporating synthetic data containing 9 major scenarios and over 4,000 distinct educational contexts. To enable comprehensive assessment, we propose a set of multi-dimensional evaluation metrics that cover 12 critical aspects relevant to both teachers and students. We further apply human annotation to ensure the effectiveness of the model-generated evaluation responses. Additionally, we succeed to train a relatively small-scale model on our constructed dataset and demonstrate that it can achieve performance comparable to state-of-the-art large models (e.g., Deepseek V3, Qwen Max) on the test set. Overall, this work provides a practical foundation for the development and evaluation of education-oriented language models."
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%0 Conference Proceedings
%T EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios
%A Xu, Bin
%A Bai, Yu
%A Sun, Huashan
%A Lin, Yiguan
%A Liu, Siming
%A Liang, Xinyue
%A Li, Yaolin
%A Dong, Zhuangzhi
%A Zhang, Jingren
%A Deng, Yufan
%A Zou, Xinyu
%A Gao, Yang
%A Huang, Heyan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F xu-etal-2026-edubench
%X As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing the first diverse benchmark tailored for educational scenarios, incorporating synthetic data containing 9 major scenarios and over 4,000 distinct educational contexts. To enable comprehensive assessment, we propose a set of multi-dimensional evaluation metrics that cover 12 critical aspects relevant to both teachers and students. We further apply human annotation to ensure the effectiveness of the model-generated evaluation responses. Additionally, we succeed to train a relatively small-scale model on our constructed dataset and demonstrate that it can achieve performance comparable to state-of-the-art large models (e.g., Deepseek V3, Qwen Max) on the test set. Overall, this work provides a practical foundation for the development and evaluation of education-oriented language models.
%U https://aclanthology.org/2026.acl-long.987/
%P 21615-21645
Markdown (Informal)
[EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios](https://aclanthology.org/2026.acl-long.987/) (Xu et al., ACL 2026)
ACL
- Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, and Heyan Huang. 2026. EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21615–21645, San Diego, California, United States. Association for Computational Linguistics.