工作量
任务(项目管理)
可靠性(半导体)
推荐系统
计算机科学
质量(理念)
人机交互
人工智能
机器学习
工程类
系统工程
功率(物理)
哲学
物理
认识论
量子力学
操作系统
作者
Aasish Bhanu,Harnish Sharma,Soumya Ranjan Pathy,Amal Ponathil,Hamed Rahimian,Kapil Chalil Madathil
标识
DOI:10.1177/21695067231193668
摘要
The use of AI-enabled recommender systems in construction activities has the potential to improve worker performance and reduce errors; however, the accuracy of such systems in providing effective suggestions is dependent on the quality of their training data. A within-subjects experimental study was conducted using a simulated recommender system for installation tasks to investigate the effect of system reliability and construction task complexity on worker trust, workload, and performance. Results indicate that overall trust in the AI agent was higher for the highly reliable condition but remained consistent across various levels of task complexity. The workload was found to be higher for low reliability and complex conditions, and the effect of reliability on performance was influenced by task complexity. These findings offer insights for designing recommender systems to support construction workers in completing procedural tasks.
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