@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Position: LLMs Can be Good Tutors in English Education
%A Ye, Jingheng
%A Wang, Shen
%A Zou, Deqing
%A Yan, Yibo
%A Wang, Kun
%A Zheng, Hai-Tao
%A Liu, Ruitong
%A Xu, Zenglin
%A King, Irwin
%A Yu, Philip S.
%A Wen, Qingsong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ye-etal-2025-position
%X 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.
%U https://aclanthology.org/2025.emnlp-main.885/
%P 17527-17546
Markdown (Informal)
[Position: LLMs Can be Good Tutors in English Education](https://aclanthology.org/2025.emnlp-main.885/) (Ye et al., EMNLP 2025)
ACL
- Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, and Qingsong Wen. 2025. Position: LLMs Can be Good Tutors in English Education. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 17527–17546, Suzhou, China. Association for Computational Linguistics.