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AuthorElkhatat, Ahmed M.
AuthorElsaid, Khaled
AuthorAlmeer, Saeed
Available date2024-09-10T06:58:03Z
Publication Date2023-12
Publication NameInternational Journal for Educational Integrity
Identifierhttp://dx.doi.org/10.1007/s40979-023-00140-5
CitationElkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19(1), 17.
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85169619524&origin=inward
URIhttp://hdl.handle.net/10576/58688
AbstractThe proliferation of artificial intelligence (AI)-generated content, particularly from models like ChatGPT, presents potential challenges to academic integrity and raises concerns about plagiarism. This study investigates the capabilities of various AI content detection tools in discerning human and AI-authored content. Fifteen paragraphs each from ChatGPT Models 3.5 and 4 on the topic of cooling towers in the engineering process and five human-witten control responses were generated for evaluation. AI content detection tools developed by OpenAI, Writer, Copyleaks, GPTZero, and CrossPlag were used to evaluate these paragraphs. Findings reveal that the AI detection tools were more accurate in identifying content generated by GPT 3.5 than GPT 4. However, when applied to human-written control responses, the tools exhibited inconsistencies, producing false positives and uncertain classifications. This study underscores the need for further development and refinement of AI content detection tools as AI-generated content becomes more sophisticated and harder to distinguish from human-written text.
SponsorThe publication of this article was funded by the Qatar National Library.
Languageen
PublisherSpringer Nature
SubjectAcademic integrity
AI content detection tools
AI-generated content
ChatGPT
Plagiarism
TitleEvaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text
TypeArticle
Issue Number1
Volume Number19
ESSN1833-2595
dc.accessType Open Access


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