Date
Publisher
arXiv
While recent efforts have begun integrating large language models (LLMs) into foreign language education (FLE), 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 FLE. 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 FLE through the thoughtful integration of LLMs.
Study at a glance
- Question: How can large language models serve as effective tutors in foreign language education?
- Setting and data: Not stated in the abstract
- Method: Position paper
Why it matters
Efforts to integrate large language models into foreign language education often rely on traditional learning tasks without fully embracing educational methodologies. Consequently, they lack adaptability to language learning.
Key findings
- In foreign language education, large language models have the potential to serve as effective tutors by acting as personalized and inclusive agents.
- Language models can serve as data enhancers by improving the creation of learning materials and simulating student interactions.
- Language models can operate as task predictors to assess student performance and optimize individual learning pathways.
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