Generational Variations in Human–AI Conversational Styles: A Computational Linguistic Analysis

Abstract

Background: While human–AI interaction has become a major part of modern digital communication, few studies have been conducted to examine intergenerational differences in users’ conversational behavior. The relationship between communication among Generation X, Millennials, and Generation Z is explored through a computational linguistic analysis. Materials and Methods: Train.csv is a large conversational dataset that was analyzed to see if there were differences between the languages used, how they interacted, and the organization of their conversations across age groups. Because the dataset lacked explicit demographic information, synthetic generational labels were generated based on linguistic features and clustering techniques that were based on the textual characteristics.  Text preprocessing, tokenization, normalization, TF–IDF vectorization, sentiment analysis, and stylistic feature extraction were all applied to the text using multiple natural language processing (NLP) methods. The factors studied included sentence length, words per sentence, the use of emojis, the use of slang, and the tendency to ask questions. Furthermore, statistical methods and machine learning algorithms were used to assess the predictiveness of the generational style when examining text-based communications.

Results and Discussion: The results show that there are certain differences in conversations between generations. Gen Z's communication is brief, casual, and simple, while Gen X's communication is formal, structured, and verbose. Millennials have a blend of both communication styles. In generational classification, the Random Forest model was the best machine learning model of the ones evaluated. Conclusion: The research emphasizes the need for AI systems to be adaptive and sensitive to the context, aiming to customize their tone and style to align with user preferences in specific sectors like healthcare, education, and digital services.