Position: LLMs Can be Good Tutors in English Education

TL;DR

This paper argues that large language models (LLMs) can serve as effective, adaptable tutors in English education by enhancing materials, assessing learners, and enabling personalized instruction, thus transforming traditional teaching methods.

Contribution

It introduces a novel framework for integrating LLMs into English education, emphasizing their roles as data enhancers, task predictors, and agents for personalized learning.

Findings

LLMs can improve learning material creation and student simulations.

LLMs can assess learner progress and optimize learning pathways.

LLMs enable personalized and inclusive English education.

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.