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English Paraphrasing Strategies and Levels of Proficiency of an AI-generated QuillBot and Paraphrasing Tool: Case Study of Scientific Research Abstracts.

Authors :
Thaweesak Chanpradit
Phakkaramai Samran
Siriprapa Saengpinit
Pailin Subkasin
Source :
Journal of English Teaching; Jun2024, Vol. 10 Issue 2, p110-126, 17p
Publication Year :
2024

Abstract

AI-generated paraphrasing tools, especially QuillBot and Paraphrasing Tool, play a crucial role in preventing plagiarism in academic writing. However, their effectiveness and proficiency have been questioned, particularly regarding the adequacy of their strategies. This qualitative study analyzed the paraphrasing strategies and proficiency levels of QuillBot and Paraphrase Tool. Using a purposive sampling technique, all 30 abstracts from one issue of the Journal of Second Language Writing were paraphrased using the two paraphrasing tools in their standard modes, and the results were analyzed using the frameworks of Keck (2014) and Nabhan et al. (2021). The results of the study indicated that both tools primarily used synonym substitution, with QuillBot favoring word-level changes and Paraphrase Tool emphasizing sentence restructuring. QuillBot tended to show minimal revision, followed by moderate revision, while Paraphrase Tool exhibited more moderate revision, followed by minimal and substantial revision. Paraphrase Tool exhibited broader paraphrasing capability than QuillBot, but both tools show some paraphrasing limitations. Overall, while these tools may enhance some writing, writers should thoroughly review the core concepts of the original texts and grammatical structures in specific contexts. For novice writers, paraphrasing practice in classrooms should be conducted under teachers' guidance. AI-generated tools should be secondary. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20879628
Volume :
10
Issue :
2
Database :
Complementary Index
Journal :
Journal of English Teaching
Publication Type :
Academic Journal
Accession number :
178433482
Full Text :
https://doi.org/10.33541/jet.v10i2.5619