计算机科学
人工智能
自然语言处理
质量(理念)
文本生成
短信
语言学
数学教育
心理学
万维网
认识论
哲学
作者
David James Woo,Hengky Susanto,Chi Ho Yeung,Kai Guo,April Ka Yeng Fung
出处
期刊:Cornell University - arXiv
日期:2023-01-01
被引量:6
标识
DOI:10.48550/arxiv.2304.02478
摘要
English as foreign language_EFL_students' use of text generated from artificial intelligence_AI_natural language generation_NLG_tools may improve their writing quality. However, it remains unclear to what extent AI-generated text in these students' writing might lead to higher-quality writing. We explored 23 Hong Kong secondary school students' attempts to write stories comprising their own words and AI-generated text. Human experts scored the stories for dimensions of content, language and organization. We analyzed the basic organization and structure and syntactic complexity of the stories' AI-generated text and performed multiple linear regression and cluster analyses. The results show the number of human words and the number of AI-generated words contribute significantly to scores. Besides, students can be grouped into competent and less competent writers who use more AI-generated text or less AI-generated text compared to their peers. Comparisons of clusters reveal some benefit of AI-generated text in improving the quality of both high-scoring students' and low-scoring students' writing. The findings can inform pedagogical strategies to use AI-generated text for EFL students' writing and to address digital divides. This study contributes designs of NLG tools and writing activities to implement AI-generated text in schools.
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