Approaching the Limits to EFL Writing Enhancement with AI-generated Text and Diverse Learners
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917946850279424 |
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| author | Woo, David James Susanto, Hengky Yeung, Chi Ho Guo, Kai |
| author_facet | Woo, David James Susanto, Hengky Yeung, Chi Ho Guo, Kai |
| contents | Generative artificial intelligence (AI) chatbots, such as ChatGPT, are reshaping how English as a foreign language (EFL) students write since students can compose texts by integrating their own words with AI-generated text. This study investigated how 59 Hong Kong secondary school students with varying levels of academic achievement interacted with AI-generated text to compose a feature article, exploring whether any interaction patterns benefited the overall quality of the article. Through content analysis, multiple linear regression and cluster analysis, we found the overall number of words -- whether AI- or human-generated -- is the main predictor of writing quality. However, the impact varies by students' competence to write independently, for instance, by using their own words accurately and coherently to compose a text, and to follow specific interaction patterns with AI-generated text. Therefore, although composing texts with human words and AI-generated text may become prevalent in EFL writing classrooms, without educators' careful attention to EFL writing pedagogy and AI literacy, high-achieving students stand to benefit more from using AI-generated text than low-achieving students. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Approaching the Limits to EFL Writing Enhancement with AI-generated Text and Diverse Learners Woo, David James Susanto, Hengky Yeung, Chi Ho Guo, Kai Computation and Language Generative artificial intelligence (AI) chatbots, such as ChatGPT, are reshaping how English as a foreign language (EFL) students write since students can compose texts by integrating their own words with AI-generated text. This study investigated how 59 Hong Kong secondary school students with varying levels of academic achievement interacted with AI-generated text to compose a feature article, exploring whether any interaction patterns benefited the overall quality of the article. Through content analysis, multiple linear regression and cluster analysis, we found the overall number of words -- whether AI- or human-generated -- is the main predictor of writing quality. However, the impact varies by students' competence to write independently, for instance, by using their own words accurately and coherently to compose a text, and to follow specific interaction patterns with AI-generated text. Therefore, although composing texts with human words and AI-generated text may become prevalent in EFL writing classrooms, without educators' careful attention to EFL writing pedagogy and AI literacy, high-achieving students stand to benefit more from using AI-generated text than low-achieving students. |
| title | Approaching the Limits to EFL Writing Enhancement with AI-generated Text and Diverse Learners |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.00367 |