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

In: Information Systems and Technological Advances for Sustainable Development

Author

Listed:
  • Mouad Berqia

    (Mohammed V University in Rabat)

  • Hafssa Benaboud

    (Mohammed V University in Rabat)

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.

Suggested Citation

  • Mouad Berqia & Hafssa Benaboud, 2024. "Detecting LLM-Enabled Plagiarism in Student Essays Using Ensemble Learning and NLP," Lecture Notes in Information Systems and Organization, in: Mohamed Ben Ahmed & Anouar Abdelhakim Boudhir & Hany Farhat Abd Elhamid Attia & Adriana Eštoková & M (ed.), Information Systems and Technological Advances for Sustainable Development, pages 376-382, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-75329-9_42
    DOI: 10.1007/978-3-031-75329-9_42
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