Classical ML Methods Show Promise in Detecting AI-Generated Text

As large language models become more capable of mimicking human writing, detecting synthetic content has grown critical for areas like academic integrity and content moderation. Researchers are revisiting classical machine learning techniques—such as logistic regression, Naive Bayes, and SVMs—as practical alternatives to computationally heavy deep learning detectors. These methods leverage linguistic features like n-gram patterns, sentence complexity, and lexical diversity to distinguish AI-generated text from human writing. Classical models achieve F1 scores in the 76–90% range, falling short of deep learning's 93–97% but offering key advantages in explainability, efficiency, and ease of deployment. Hybrid approaches that combine classical models with lightweight neural networks are seen as a promising path forward for resource-constrained environments.