@inproceedings{ye-etal-2025-position,
    title = "Position: {LLM}s Can be Good Tutors in {E}nglish Education",
    author = "Ye, Jingheng  and
      Wang, Shen  and
      Zou, Deqing  and
      Yan, Yibo  and
      Wang, Kun  and
      Zheng, Hai-Tao  and
      Liu, Ruitong  and
      Xu, Zenglin  and
      King, Irwin  and
      Yu, Philip S.  and
      Wen, Qingsong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.885/",
    pages = "17527--17546",
    ISBN = "979-8-89176-332-6",
    abstract = "While recent efforts have begun integrating large language models (LLMs) into English education, they often rely on traditional approaches to learning tasks without fully embracing educational methodologies, thus lacking adaptability to language learning. To address this gap, we argue that **LLMs have the potential to serve as effective tutors in English Education**. Specifically, LLMs can play three critical roles: (1) as data enhancers, improving the creation of learning materials or serving as student simulations; (2) as task predictors, serving as learner assessment or optimizing learning pathway; and (3) as agents, enabling personalized and inclusive education. We encourage interdisciplinary research to explore these roles, fostering innovation while addressing challenges and risks, ultimately advancing English Education through the thoughtful integration of LLMs."
}