Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
An Empirical Study on the Application of ChatGPT in Writing Learning for English Major Students
1
Zitationen
1
Autoren
2025
Jahr
Abstract
With the widespread use of artificial intelligence tools such as ChatGPT in English writing, the academic community has begun to pay attention to its potential in improving writing efficiency and quality. However, there is still a lack of research on its application in actual teaching, especially in terms of grammatical accuracy and academic integrity. This study aims to explore the impact of ChatGPT on English writing teaching, especially its role in improving students’ writing efficiency, autonomous learning ability and writing quality. In the study, students in the experimental group use ChatGPT to obtain writing suggestions, grammar revisions, content expansion and other support during the writing process. The researchers conduct a before-and-after comparative analysis by recording students’ writing time, revision time, completion rate and teacher scores. The results showed that after using ChatGPT, students’ writing efficiency was significantly improved, the average writing time was shortened by 35 minutes, and the revision time was also reduced accordingly. In terms of teacher ratings, teachers’ scores on student writing increased by an average of 1.26 points. The results of the study indicate that AI tools play a positive role in improving writing quality. In addition, students’ independent learning ability and problem-solving ability had also been significantly improved, with an average increase of 1.89 points and 1.36 points, respectively. Overall, this tool has significant advantages in improving writing efficiency and quality, and can effectively support students’ independent learning. However, in terms of academic integrity and grammatical accuracy, further in-depth exploration and optimization are still needed.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.418 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.288 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.726 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.516 Zit.