A System Dynamics Approach to Modeling Trust Dynamics in Conversational AI

Abstract

Abstract Trust plays a central role in how users adopt and rely on conversational AI systems. Research shows that trust does not develop linearly or remains stable over time. Although initially it often increases, it can fluctuate, decline, and even collapse over time, even when the AI’s performance remains constant. Because existing approaches often conceptualize trust as a stable outcome or direct response to specific system cues, they struggle to explain these longer-term patterns of change. In this paper, we conceptualize trust in conversational AI as a dynamic self-regulating process shaped by repeated interaction and feedback mechanisms. Using a system dynamics perspective, we propose a conceptual model grounded in the "Limits to Success" archetype and implement it as a computer simulation. By offering a process-oriented and mechanism-based account of trust development, this work advances trust research beyond static approaches and highlights trust calibration as a dynamic, ongoing process with clear design implications.