Research on AI-Generated Text Detection Based on Machine Learning Models

Abstract

The purpose of this research is to ensure the authenticity of information, guarantee the reliability and credibility of information sources, and prevent the spread of false information, fabricated data, and misleading content. In the academic field, detecting AI-generated papers, articles, and assignments helps maintain academic integrity, prevent academic fraud and plagiarism, and thus improve academic capabilities. This study summarize the characteristics of the three selected models, which are Logistic Regression, Support Vector Machine (SVM), and Naive Bayes (NB) Classifier. And provide recommendations and directions for improvement in the choice of detection models for AI-generated content. Through comparison of three models—logistic regression, SVM, and Naive Bayes—on the same dataset in terms of Accuracy, Precision, and F1-score, it is determined that logistic regression performs the best for this type of dataset. Logistic regression achieves superior performance with metrics exceeding 90%. SVM shows suitability for large datasets with metrics around 70% in this dataset. However, Naive Bayes, typically suitable for smaller datasets, performs poorly on this dataset, achieving only 50% accuracy.