A comparative study of thematic choices and thematic progression patterns in human-written and AI-generated texts

Abstract

By adopting systemic functional grammar as the theoretical framework, the present research investigates the choices of the textual, the interpersonal, the topical Themes, and thematic progression patterns in human-written and AI-generated argumentative texts to examine how the human author and the robot organize the clause and text. The findings suggest that human-written and AI-generated texts differ significantly in using the subtypes of textual, interpersonal, and marked topical Themes. In terms of textual Themes, the machine tends to use more concession signals to repeat information that has been stated earlier, adopt fewer condition signals since the robot may be less likely to imagine possible or impossible situations, and repetitively rely on clause-initial conjunctive adjuncts of addition to extend the clause to the previous text. As for interpersonal Themes, the robot seldom adopts modal adjuncts or modal verbal operators as interpersonal Themes, which may suggest its lack of awareness of interaction with the reader and its avoidance of expressing its viewpoints in a typical or congruent way. Regarding topical Themes, the less use of marked themes in AI-generated texts demonstrates that AI may less carefully plan the development of the text to foreground the setting or construct coherent text. Considering thematic progression patterns, the frequent use of the constant pattern in AI-generated texts prevents the text from development and makes the text redundant and simplistic like a list of ideas.