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Gotcha GPT: Ensuring the Integrity in Academic Writing.

João Gabriel Gralha1, André Silva Pimentel1

  • 1Departamento de Química, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ 22453-900, Brazil.

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Summary
This summary is machine-generated.

This study introduces a method to detect Artificial Intelligence (AI)-generated academic writing using machine learning classifiers. The developed models achieve high accuracy, aiding in maintaining academic integrity in scholarly publications.

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Area of Science:

  • Computer Science
  • Natural Language Processing
  • Academic Publishing

Background:

  • The rapid advancement of Artificial Intelligence (AI) presents challenges to ensuring the integrity of academic writing.
  • Distinguishing between AI-generated and human-generated manuscripts is crucial for universities and publishers.
  • Existing methods may not be sufficient to address AI's growing capabilities in text generation.

Purpose of the Study:

  • To develop and evaluate machine learning models for differentiating AI-generated from human-generated academic text.
  • To provide a reliable tool for academics and publishers to verify manuscript authorship.
  • To offer practical solutions for maintaining academic integrity in the age of AI.

Main Methods:

  • Utilized classifier models including decision tree, random forest, extra trees, and AdaBoost.
  • Employed Scikit learn libraries for statistical evaluation (precision, accuracy, recall, F1, MCC, Cohen's kappa) and confusion matrix analysis.
  • Trained and tested models on a dataset of approximately 400 AI-generated and 400 human-generated scientific manuscript texts with a 50/50 random split.

Main Results:

  • Model evaluation accuracy for classification ranged from 0.97 to 0.99.
  • The employed statistical metrics and confusion matrix provided high confidence in the model's performance.
  • The models demonstrated a strong capability to distinguish between AI and human-generated text.

Conclusions:

  • The developed classifier models are effective in identifying AI-generated academic writing with high accuracy.
  • This approach offers a valuable tool for safeguarding academic integrity in scholarly publications.
  • Freely available tutorials and code (Gotcha GPT) support the practical application of these methods.