大模型可当英语老师，三重角色提升教学效果
Position: LLMs Can be Good Tutors in English Education
- 将大模型作为学习材料生成器、学习路径预测者和个性化教学代理
- 能模拟学生行为，优化学习路径，支持因材施教
- 适合教育科技研究者与智能辅导系统开发者参考
尽管近期已有将大语言模型（LLMs）引入英语教育的尝试，但大多仍沿用传统学习任务设计，未充分结合教育方法学，缺乏对语言学习的适应性。为弥补这一差距，我们认为大模型具备成为有效英语导师的潜力，可承担三重关键角色：(1) 数据增强者，用于改进学习材料生成或模拟学生行为；(2) 任务预测者，用于学习者评估或优化学习路径；(3) 教学代理，实现个性化与包容性教育。我们呼吁跨学科研究探索这些角色，推动创新并应对挑战与风险，最终通过审慎整合大模型促进英语教育发展。
原文摘要 · Abstract (English)
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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