Analyzing Word Frequency and Predictive Patterns in AI-Generated Essays

Artificial Intelligence (AI) has dramatically transformed various aspects of human life and activities, including the composition of essays and texts.AI technologies have enabled computers to generate text that closely resembles human writing and this has raised concerns with implications for academic integrity, creative authenticity, and professional communication.This study aim to investigates the linguistic characteristics and predictive mechanisms underlying AIgenerated essays, aiming to identify markers that distinguish them from human-authored texts.1,000 essays with diverse topics and writing styles were generated using ChatGPT, DeepSeek, and Gemini and a comparable corpus of humanwritten essays were also collected from publicly available sources.The research work used natural language processing (NLP) techniques and machine learning models to analyze word frequency, next-word prediction patterns, and stylistic elements in a corpus of AI-generated and human-written essays.The results show that the temperature settings in AI models significantly influence word selection, with higher temperatures increasing randomness and reducing the likelihood of predictable word choices.Machine learning classification using Support Vector Machines (SVM) of 98% and Random Forests of 95.75% achieved high accuracy in differentiating between AI and human essays, highlighting the effectiveness of linguistic features for automated detection.The study concludes that AI-generated content can be reliably distinguished from human writing using stylistic and lexical features, contributing to the development of more reliable AI assessment tools and a better understanding of NLP model behavior.