The guarded engagement loop: risk salience and interaction-driven underperformance in generative AI adoption

Abstract

Generative AI adoption is often framed primarily as a question of learning technical skills. It is thought that if users learn better prompting and evaluation practices, useful outputs will follow, leading to greater reliance on the technology. This perspective overlooks a defining feature of large language models (LLMs): their output quality depends heavily on how users engage with them. Because LLM performance varies substantially with depth of disclosure, contextual richness, and iterative refinement, user interaction strategies directly shape perceived usefulness and observed performance. This paper develops a conceptual framework that proposes how risk salience may shape these interaction dynamics. Drawing on research in trust in automation, privacy calculus, algorithm aversion, and the social amplification of risk, we propose the guarded engagement loop, a multilevel feedback mechanism in which risk perceptions may shape interaction strategies that influence observed performance and, in turn, recalibrate trust in generative AI systems. At the micro level, elevated risk salience related to privacy, safety, or ethical concerns may lead users to adopt guarded interaction strategies characterized by reduced contextual disclosure and limited iteration. These constrained interactions can lower output quality and increase the likelihood of visible errors, which may further erode trust and reinforce cautious engagement. At the macro level, values-driven withdrawal from AI use has the potential to narrow the diversity of visible applications, amplifying risk-focused narratives, reinforcing perceptions of harm in public discourse. The guarded engagement loop framework conceptualizes generative AI adoption as a feedback process in which risk perceptions may shape interaction conditions that, in turn, can influence observed performance and subsequent trust calibration. We articulate testable propositions and discuss implications for organizational governance, AI system design, and institutional conditions that enable bounded openness and calibrated reliance.