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Large language models have demonstrated exceptional capabilities in tasksinvolving natural language generation, reasoning, and comprehension. This studyaims to construct prompts and comments grounded in the diverse scoring criteriadelineated within the official TOEFL guide. The primary objective is to assessthe capabilities and constraints of ChatGPT, a prominent representative oflarge language models, within the context of automated essay scoring. Theprevailing methodologies for automated essay scoring involve the utilization ofdeep neural networks, statistical machine learning techniques, and fine-tuningpre-trained models. However, these techniques face challenges when applied todifferent contexts or subjects, primarily due to their substantial datarequirements and limited adaptability to small sample sizes. In contrast, thisstudy employs ChatGPT to conduct an automated evaluation of English essays,even with a small sample size, employing an experimental approach. Theempirical findings indicate that ChatGPT can provide operational functionalityfor automated essay scoring, although the results exhibit a regression effect.It is imperative to underscore that the effective design and implementation ofChatGPT prompts necessitate a profound domain expertise and technicalproficiency, as these prompts are subject to specific threshold criteria.Keywords: ChatGPT, Automated Essay Scoring, Prompt Learning, TOEFL IndependentWriting Task

Empirical Study of Large Language Models as Automated Essay Scoring Tools in English Composition__Taking TOEFL Independent Writing Task for Example