Identifying ChatGPT-generated texts in EFL students’ writing: Through comparative analysis of linguistic fingerprints

The emergence of generative AI (GenAI) poses new challenges for L2 writing teachers. This study investigates the distinguishability of essays written by Japanese EFL learners from those generated by ChatGPT. Partially replicating Herbold et al. (2023), 140 first-year university students wrote essays and completed a survey on ChatGPT use. Among them, 125 wrote independently, 13 used ChatGPT for proofreading, and two asked ChatGPT to write the entire essay. To create a comparative dataset, 123 additional essays were generated by ChatGPT, imitating the two texts. The resulting 263 essays were then analyzed using the natural language processing (NLP) technique, including automated linguistic analysis and machine learning classification using random forest. The results reveal significant differences between human-written and ChatGPT-generated essays across all linguistic features, with the latter being easily identifiable. This study emphasizes the need for clear guidelines on the ethical use of AI in L2 writing, highlighting the potential risk of inappropriate AI use and the importance of fostering a mutual understanding of AI use with learners regarding responsible AI integration in academic work.

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Publication Details

Journal
Applied Corpus Linguistics
Published
2024-09-26
DOI
https://doi.org/10.1016/j.acorp.2024.100106
Citations
37
Primary Topic
Text Readability and Simplification
Type
article
Field-Weighted Citation Impact
12.85
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article

Identifying ChatGPT-generated texts in EFL students’ writing: Through comparative analysis of linguistic fingerprints

Yu Tamura, Atsushi Mizumoto, Sachiko Yasuda
37 citations
Applied Corpus Linguistics
Text Readability and Simplification
12.85
article

Identifying ChatGPT-generated texts in EFL students’ writing: Through comparative analysis of linguistic fingerprints

Yu Tamura, Atsushi Mizumoto, Sachiko Yasuda
article en
37 citations

Abstract

The emergence of generative AI (GenAI) poses new challenges for L2 writing teachers. This study investigates the distinguishability of essays written by Japanese EFL learners from those generated by ChatGPT. Partially replicating Herbold et al. (2023), 140 first-year university students wrote essays and completed a survey on ChatGPT use. Among them, 125 wrote independently, 13 used ChatGPT for proofreading, and two asked ChatGPT to write the entire essay. To create a comparative dataset, 123 additional essays were generated by ChatGPT, imitating the two texts. The resulting 263 essays were then analyzed using the natural language processing (NLP) technique, including automated linguistic analysis and machine learning classification using random forest. The results reveal significant differences between human-written and ChatGPT-generated essays across all linguistic features, with the latter being easily identifiable. This study emphasizes the need for clear guidelines on the ethical use of AI in L2 writing, highlighting the potential risk of inappropriate AI use and the importance of fostering a mutual understanding of AI use with learners regarding responsible AI integration in academic work.

Applied Corpus LinguisticsVol. 4(3)
Kansai University (JP), Kobe University (JP)
Quality Education
Openalex Percentile: Top 1%
Text Readability and Simplification
12.85
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Identifying ChatGPT-generated texts in EFL students’ writing: Through comparative analysis of linguistic fingerprints — Yu Tamura, Atsushi Mizumoto, et al. · Applied Corpus Linguistics (2024) | TGRS Research Map | TGRS