{"title":"人工撰写与chatgpt生成文本:英文研究文章摘要的立场","authors":"Man Zhang , Jiawei Zhang","doi":"10.1016/j.system.2025.103842","DOIUrl":null,"url":null,"abstract":"<div><div>While ChatGPT demonstrates great potential as a writing assistant tool, it is not without its limitations. This article investigates the use of stance in human-written and ChatGPT-generated abstracts to evaluate the performance of ChatGPT in generating academic texts. It built a corpus of 600 human-written and 600 ChatGPT-generated abstracts, adopted a theory-informed and corpus-driven approach and a mixed-methods design, constructed a system of stance, and explored the stance use in the corpus. Stance, referring to the writer's judgments, evaluations, assessments, and feelings, can be interpreted as a three-dimensional system: type (epistemic, affective), value (high: boosters and positive affect, low: hedges and negative affect), and orientation (explicit, implicit). In both types of abstracts, stance markers are mainly used to express the epistemic and positive attitudes implicitly. However, compared to the stance in human-written abstracts, those in ChatGPT-generated abstracts are significantly more affect-oriented. The similarities and differences in the stance use between the two types of abstracts are ascribable to the nature of abstracts and the underlying mechanism of ChatGPT. This study provides a theoretical framework for understanding and analyzing stance in texts. The findings offer implications for academic English writing and teaching, and the advancement and innovation of AI.</div></div>","PeriodicalId":48185,"journal":{"name":"System","volume":"134 ","pages":"Article 103842"},"PeriodicalIF":5.6000,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Human-written vs. ChatGPT-generated texts: Stance in English research article abstracts\",\"authors\":\"Man Zhang , Jiawei Zhang\",\"doi\":\"10.1016/j.system.2025.103842\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>While ChatGPT demonstrates great potential as a writing assistant tool, it is not without its limitations. This article investigates the use of stance in human-written and ChatGPT-generated abstracts to evaluate the performance of ChatGPT in generating academic texts. It built a corpus of 600 human-written and 600 ChatGPT-generated abstracts, adopted a theory-informed and corpus-driven approach and a mixed-methods design, constructed a system of stance, and explored the stance use in the corpus. Stance, referring to the writer's judgments, evaluations, assessments, and feelings, can be interpreted as a three-dimensional system: type (epistemic, affective), value (high: boosters and positive affect, low: hedges and negative affect), and orientation (explicit, implicit). In both types of abstracts, stance markers are mainly used to express the epistemic and positive attitudes implicitly. However, compared to the stance in human-written abstracts, those in ChatGPT-generated abstracts are significantly more affect-oriented. The similarities and differences in the stance use between the two types of abstracts are ascribable to the nature of abstracts and the underlying mechanism of ChatGPT. This study provides a theoretical framework for understanding and analyzing stance in texts. The findings offer implications for academic English writing and teaching, and the advancement and innovation of AI.</div></div>\",\"PeriodicalId\":48185,\"journal\":{\"name\":\"System\",\"volume\":\"134 \",\"pages\":\"Article 103842\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"System\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0346251X25002520\",\"RegionNum\":1,\"RegionCategory\":\"文学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"System","FirstCategoryId":"98","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0346251X25002520","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Human-written vs. ChatGPT-generated texts: Stance in English research article abstracts
While ChatGPT demonstrates great potential as a writing assistant tool, it is not without its limitations. This article investigates the use of stance in human-written and ChatGPT-generated abstracts to evaluate the performance of ChatGPT in generating academic texts. It built a corpus of 600 human-written and 600 ChatGPT-generated abstracts, adopted a theory-informed and corpus-driven approach and a mixed-methods design, constructed a system of stance, and explored the stance use in the corpus. Stance, referring to the writer's judgments, evaluations, assessments, and feelings, can be interpreted as a three-dimensional system: type (epistemic, affective), value (high: boosters and positive affect, low: hedges and negative affect), and orientation (explicit, implicit). In both types of abstracts, stance markers are mainly used to express the epistemic and positive attitudes implicitly. However, compared to the stance in human-written abstracts, those in ChatGPT-generated abstracts are significantly more affect-oriented. The similarities and differences in the stance use between the two types of abstracts are ascribable to the nature of abstracts and the underlying mechanism of ChatGPT. This study provides a theoretical framework for understanding and analyzing stance in texts. The findings offer implications for academic English writing and teaching, and the advancement and innovation of AI.
期刊介绍:
This international journal is devoted to the applications of educational technology and applied linguistics to problems of foreign language teaching and learning. Attention is paid to all languages and to problems associated with the study and teaching of English as a second or foreign language. The journal serves as a vehicle of expression for colleagues in developing countries. System prefers its contributors to provide articles which have a sound theoretical base with a visible practical application which can be generalized. The review section may take up works of a more theoretical nature to broaden the background.