Detecting LLM-Enabled Plagiarism in Student Essays Using Ensemble Learning and NLP

Mouad Berqia () and Hafssa Benaboud ()
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Mouad Berqia: Mohammed V University in Rabat
Hafssa Benaboud: Mohammed V University in Rabat

A chapter in Information Systems and Technological Advances for Sustainable Development, 2024, pp 376-382 from Springer

Abstract: Abstract The increasing use of Large Language Models (LLMs) is causing concern about their ability to replace human jobs. Educators are especially worried about how these models might affect students, specifically their ability to do their own assignments like essays. This paper addresses the fear that LLMs might lead to more plagiarism in schools. We propose a machine learning model to distinguish between essays written by middle and high school students and those generated by LLMs. The proposed method uses Natural Language Processing (NLP) and Ensemble Learning techniques. The proposed model performed well, with a ROC AUC score of 0.9985.

Keywords: Large Language Models; Ensemble Learning; Natural Language Processing; Plagiarism; Academic Integrity (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-75329-9_42

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DOI: 10.1007/978-3-031-75329-9_42

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