Behavioral Indicators of Overreliance During Interaction with Conversational Language Models

The paper tackles a foundational problem for product directors working with AI: how do you know when users are trusting the system too much? Traditional metrics miss the behavioral patterns that emerge during interaction — the micro-decisions, hesitations, and confidence signals that reveal cognitive overload or misplaced trust. This connects directly to Simon's work on bounded rationality and satisficing: users don't optimize their relationship with AI tools, they develop heuristics that can systematically fail. The behavioral indicators framework offers practical tools for product teams to instrument AI interfaces not just for task completion but for appropriate calibration of human-AI collaboration. Understanding these patterns becomes critical as AI moves from occasional tool to constant collaborator.