  The emergence of large language models (LLMs) capable of generating realistic
texts and images has sparked ethical concerns across various sectors. In
response, researchers in academia and industry are actively exploring methods
to distinguish AI-generated content from human-authored material. However, a
crucial question remains: What are the unique characteristics of AI-generated
text? Addressing this gap, this study proposes StyloAI, a data-driven model
that uses 31 stylometric features to identify AI-generated texts by applying a
Random Forest classifier on two multi-domain datasets. StyloAI achieves
accuracy rates of 81% and 98% on the test set of the AuTextification dataset
and the Education dataset, respectively. This approach surpasses the
performance of existing state-of-the-art models and provides valuable insights
into the differences between AI-generated and human-authored texts.
