On the application of Large Language Models for language teaching and assessment technology

This paper examines applications of large language models (LLMs like GPT-4, PaLM) for language teaching and assessment technology, covering content generation, automated grading, grammatical error correction, and personalized feedback. The work focuses on language learning contexts (not K-12 specifically) and evaluates LLM performance on tasks like question difficulty estimation, text generation quality, automated assessment, and error correction using established benchmarks.

The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented impact in the popular media and public consciousness, giving rise to a mixture of excitement and fear as to their capabilities and potential uses, and shining a light on natural language processing research which had not previously received so much attention. The developments offer great promise for education technology, and in this paper we look specifically at the potential for incorporating large

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