Exploring Generative AI-User Interactions through Self-Programming and Structural Coupling in Luhmann's Systems Theory

This paper explores the concepts of self-programming and structural coupling between generative AI (Gen AI) and users, grounded in Luhmann's systems theory. By tracing the evolution of generative AI from early models to advanced architectures such as foundation models, the paper shows how these systems approximate contextual understanding and respond dynamically to user inputs. The concept of self-programming is extended to users, who refine their engagement strategies through repeated interactions, developing prompt scripts and styles that enhance the relevance and utility of AI outputs. The study highlights the recursive feedback loop between AI and users, wherein both entities mutually influence and adapt each other's operations. It further investigates the structural coupling of these interactions, focusing on the paradoxical interplay between dependence and independence, as well as the shared textual medium that evolves through user inputs and AI responses. Additionally, the paper identifies excluded thirds (e.g., authenticity and engineer) and persistent paradoxes in AI-mediated interactions, particularly the tensions between specificity and generality, as well as novelty and familiarity. The research calls for further exploration of these paradoxes to promote more meaningful and adaptive forms of AI-user interactions.