How Human–AI Interaction Reshapes Construction Workers’ Safety Behaviors: Insights from Agent-Based Modeling

Abstract The complex nature of human–artificial intelligence (AI) interaction (HAI) introduces new challenges for worker safety. While ensuring human well-being in HAI has become a shared objective, the lack of quantifiable approaches limits our ability to capture its nuanced effects on workers. To address this gap, the present study develops an agent-based modeling framework grounded in stimulus-organism-response (SOR) theory. The model simulates workers’ interactions with AI systems and peers within a bounded two-dimensional space, tracking the resulting cognitive, emotional, and behavioral responses. It comprises three components: worker-agent design, SOR process modeling, and an interaction rule base. Experiments were conducted under three scenarios: (1) repeated simulations with balanced parameter settings, (2) batch simulations with varying parameter settings, and (3) empirical validation at a real smart construction site. Results demonstrate that workers’ safety behavior progressively deteriorates as HAI deepens, with extra-role safety behavior exhibiting the most pronounced decline. This study extends the application of SOR theory to the construction industry and deepens the understanding of HAI in the context of construction safety. Practically, it underscores the cumulative cognitive and emotional effects of HAI on workers, providing both a managerial basis for defining human–AI boundaries and a quantifiable tool for long-term safety decision-making.