Using AI-Based Detectors to Control AI-Assisted Plagiarism in ESL Writing: "The Terminator versus the Machines"

Autor/in	Ibrahim, Karim
Titel	Using AI-Based Detectors to Control AI-Assisted Plagiarism in ESL Writing: "The Terminator versus the Machines"
Quelle	In: Language Testing in Asia, 13 (2023), Artikel 46 (28 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ibrahim, Karim)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s40468-023-00260-2
Schlagwörter	English (Second Language); Second Language Learning; Second Language Instruction; Artificial Intelligence; Plagiarism; Writing Evaluation; Computational Linguistics; Interdisciplinary Approach; Accuracy; Identification; Essays; Computer Software + Suchen Sie Ihr Suchwort? English as second language; English; Second Language; Englisch als Zweitsprache; Zweitsprachenerwerb; Fremdsprachenunterricht; Künstliche Intelligenz; Plagiat; Linguistics; Computerlinguistik; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Identifikation; Identifizierung; Essay; Aufsatzunterricht
Abstract	The release of ChatGPT marked the beginning of a new era of AI-assisted plagiarism that disrupts traditional assessment practices in ESL composition. In the face of this challenge, educators are left with little guidance in controlling AI-assisted plagiarism, especially when conventional methods fail to detect AI-generated texts. One approach to managing AI-assisted plagiarism is using fine-tuned AI classifiers, such as RoBERTa, to identify machine-generated texts; however, the reliability of this approach is yet to be established. To address the challenge of AI-assisted plagiarism in ESL contexts, the present cross-disciplinary descriptive study examined the potential of two RoBERTa-based classifiers to control AI-assisted plagiarism on a dataset of 240 human-written and ChatGPT-generated essays. Data analysis revealed that both platforms could identify AI-generated texts, but their detection accuracy was inconsistent across the dataset. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01