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Position: LLMs Can be Good Tutors in English Education

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arxiv 2502.05467 v2 pith:2B2BKM3O submitted 2025-02-08 cs.CL cs.AI

classification cs.CLcs.AI
keywords educationllmsenglishlearninglanguagerolesservingtutors
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Survey on LLM-based Conversational User Simulation

    cs.CL 2026-04 unverdicted novelty 6.0 of 10

    A survey that introduces a taxonomy for LLM-based conversational user simulation, analyzes core techniques and evaluation methods, and identifies open challenges in the field.

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