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The Ability of ChatGPT in Paraphrasing Texts and Reducing Plagiarism: A Descriptive Analysis.

Authors :
Hassanipour S
Nayak S
Bozorgi A
Keivanlou MH
Dave T
Alotaibi A
Joukar F
Mellatdoust P
Bakhshi A
Kuriyakose D
Polisetty LD
Chimpiri M
Amini-Salehi E
Source :
JMIR medical education [JMIR Med Educ] 2024 Jul 08; Vol. 10, pp. e53308. Date of Electronic Publication: 2024 Jul 08.
Publication Year :
2024

Abstract

Background: The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out.<br />Objective: This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot.<br />Methods: Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated.<br />Results: The average plagiarism rate in the texts generated by ChatGPT was 45% (SD 10%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference -0.51, 95% CI -0.54 to -0.48; P<.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference -0.06, 95% CI -0.08 to -0.03; P<.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P<.001).<br />Conclusions: Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work.<br /> (© Soheil Hassanipour, Sandeep Nayak, Ali Bozorgi, Mohammad-Hossein Keivanlou, Tirth Dave, Abdulhadi Alotaibi, Farahnaz Joukar, Parinaz Mellatdoust, Arash Bakhshi, Dona kuriyakose, Lakshmi Polisetty, Mallika Chimpiri, Ehsan Amini-Salehi. Originally published in JMIR Medical Education (https://mededu.jmir.org).)

Subjects

Subjects :
Humans
Writing
Plagiarism

Details

Language :
English
ISSN :
2369-3762
Volume :
10
Database :
MEDLINE
Journal :
JMIR medical education
Publication Type :
Academic Journal
Accession number :
38989841
Full Text :
https://doi.org/10.2196/53308