  This research explores the nuanced differences in texts produced by AI and
those written by humans, aiming to elucidate how language is expressed
differently by AI and humans. Through comprehensive statistical data analysis,
the study investigates various linguistic traits, patterns of creativity, and
potential biases inherent in human-written and AI- generated texts. The
significance of this research lies in its contribution to understanding AI's
creative capabilities and its impact on literature, communication, and societal
frameworks. By examining a meticulously curated dataset comprising 500K essays
spanning diverse topics and genres, generated by LLMs, or written by humans,
the study uncovers the deeper layers of linguistic expression and provides
insights into the cognitive processes underlying both AI and human-driven
textual compositions. The analysis revealed that human-authored essays tend to
have a higher total word count on average than AI-generated essays but have a
shorter average word length compared to AI- generated essays, and while both
groups exhibit high levels of fluency, the vocabulary diversity of Human
authored content is higher than AI generated content. However, AI- generated
essays show a slightly higher level of novelty, suggesting the potential for
generating more original content through AI systems. The paper addresses
challenges in assessing the language generation capabilities of AI models and
emphasizes the importance of datasets that reflect the complexities of human-AI
collaborative writing. Through systematic preprocessing and rigorous
statistical analysis, this study offers valuable insights into the evolving
landscape of AI-generated content and informs future developments in natural
language processing (NLP).
