Trust in Artificial Intelligent Agent while Completing a Procedural Construction Task

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 wa...

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Bibliographic Details
Published inProceedings of the Human Factors and Ergonomics Society Annual Meeting Vol. 67; no. 1; pp. 2005 - 2006
Main Authors Bhanu, Aasish, Sharma, Harnish, Pathy, Soumya Ranjan, Ponathil, Amal, Rahimian, Hamed, Madathil, Kapil Chalil
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.09.2023
Subjects
Construction task
Augmented Reality
Artificial Intelligence
AI agent
Procedural task
Performance
Trust
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ISSN1071-1813
2169-5067
DOI10.1177/21695067231193668

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Summary: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.
ISSN:1071-1813
2169-5067
DOI:10.1177/21695067231193668