Human-AI Text Classifier Using Feature Vectorization and ML Models

The ability to discern between text produced by artificial intelligence (AI) and that created by humans has become a crucial challenge in domains including academia, media, and digital communications due to the increasing popularity of AI and NLP technologies. With an emphasis on human response identification, this paper provides an extensive overview of the state-of-the-art techniques and algorithms for identifying AI-generated material. This paper assesses important detection methods, such as deep learning models, attention mechanisms, and feature extraction algorithms, and identifies research needs through a thorough literature review. To shed light on the continuous difficulties in AI-generated text identification, we also investigate the precision, resilience, and practicality of these techniques.