@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://preview.aclanthology.org/ingest-acl/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."
}