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Position: LLMs Can be Good Tutors in English Education
Created by
Haebom
Author
Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen
Outline
This paper highlights the recent integration of large-scale language models (LLMs) into English language education, but their reliance on traditional learning methods hinders their adaptability. We argue that LLMs can serve as effective tutors in English language education, demonstrating their potential to play three crucial roles: data enhancement (improving learning material generation or student simulation), task prediction (learner assessment or learning path optimization), and agent-based learning (enabling personalized and comprehensive education). We emphasize the importance of fostering innovation and addressing challenges and risks through interdisciplinary research to effectively integrate LLMs.
Takeaways, Limitations
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Takeaways:
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Leveraging LLM to Offer New Possibilities in English Education (Data Enhancement, Task Prediction, and Personalized Learning)
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Presenting the possibility of innovation in English education through multidisciplinary research.
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The potential for developing a personalized and comprehensive English education system based on LLM
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Limitations:
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Lack of specific methodological suggestions for applying LLM to educational contexts.
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Lack of in-depth discussion of the risks and ethical issues of using LLMs.
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Technical challenges in developing and implementing an LLM-based education system
