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TRUVACE RECORD VERSION
record: TRV-2026-2488
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:41:22.492899Z
status: published
lens: p_space
sector: labor
headline: How Human–AI Interaction Reshapes Construction Workers’ Safety Behaviors: Insights from Agent-Based Modeling
dek: 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…
gain_title: (none)
problem_title: In an agent-based simulation validated at a smart construction site, workers' safety behavior steadily declined as their interaction with AI systems deepened, with extra-role safety behavior falling most.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In an agent-based simulation validated at a smart construction site, workers' safety behavior steadily declined as their interaction with AI systems deepened, with extra-role safety behavior falling most.
problem_evidence: workers' safety behavior progressively deteriorates as HAI deepens, with extra-role safety behavior exhibiting the most pronounced decline
quick_read: A modeling study in the Journal of Construction Engineering and Management simulated how construction workers respond to working alongside AI systems and their peers. It found that safety behavior got steadily worse as human-AI interaction deepened, and that extra-role safety behavior, meaning safety actions beyond formal job duties, dropped the most.

The researchers built an agent-based model grounded in stimulus-organism-response theory. Simulated workers moved in a bounded two-dimensional space, and the model tracked their cognitive, emotional and behavioral responses to AI systems and colleagues.

The authors say the results point to cumulative cognitive and emotional effects of working with AI. They propose the model as a basis for deciding where human-AI boundaries should sit and as a tool for long-term safety decisions on construction sites.
limitation: The findings come mainly from an agent-based simulation in a bounded two-dimensional space, with empirical validation at one smart construction site.
tag: Evidence-backed problem
key_points: The study built an agent-based model of workers interacting with AI systems and peers in a bounded two-dimensional space. | The model draws on stimulus-organism-response theory and has three components: worker-agent design, SOR process modeling and an interaction rule base. | Three experiments were run: repeated simulations with balanced parameters, batch simulations with varying parameters, and validation at a real smart construction site. | Workers' safety behavior progressively deteriorated as human-AI interaction deepened. | Extra-role safety behavior showed the sharpest decline. | The authors present the model as a quantifiable tool for setting human-AI boundaries and making long-term safety decisions.
rundown: The study addresses a gap the authors describe: although protecting worker well-being in human-AI interaction is a shared goal, there has been no quantifiable way to capture its effects on workers. Their answer is a simulation framework that tracks how AI exposure shapes workers' thinking, feelings and conduct.

The experiments covered three scenarios. The first used repeated runs with balanced parameter settings, the second used batch runs with varied settings, and the third checked the model against a real smart construction site.

Across the scenarios, safety behavior worsened as interaction with AI grew deeper. The authors say this reflects cumulative cognitive and emotional effects, and they suggest managers could use the model to define limits on human-AI interaction.
sources:
- peer_reviewed | Journal of Construction Engineering and Management | https://doi.org/10.1061/jcemd4.coeng-18562 | 2026-11-05
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