The construction industry in the United States accounts for nearly 15% of Gross Domestic Product and over 6% of the total workforce. The construction industry is responsible for renewing US infrastructure to meet daily needs for clean water, transportation, and living space. Recently, construction has experienced significant workforce shortages that have precipitated the need to deliver craft workers with optimal information to construct infrastructure efficiently, safely, and in a manner that promotes future workers' social and economic well-being. A growing body of literature has demonstrated the exciting potential of augmented reality (AR) and artificial intelligence (AI) to transform workplaces, but most existing studies focus on office, factory, and medical workers. This project aims to explore if and how these technologies specifically improve work performance in construction. In current construction industry practice, design information is provided to construction workers through two-dimensional plans and written specifications. New technology is beginning to enable design information to be represented in multi-dimensional models that simulate the construction process and aid with visualization. As AR and AI technologies mature, it is posited that they can promote more effective construction via enhanced access to information. However, it is unclear how innovative information should be presented with AR to best support future construction work. Additionally, as AI is considered for the creation of project information, we must understand how the perceived origin of a design from either humans or AI impacts the user's trust of the information and self-confidence in subsequent decisions.
In this project, researchers will test two main hypotheses: (1) How and to what extent the level of detail of design information delivered in AR impacts dimensions of work performance and (2) How and to what extent the perceived origin of design information impacts trust in the information and self-confidence in decisions. Secondary hypotheses will test how level of detail and perceived origin of design impact spatial reasoning; methods and timing of planning; and anticipation of errors and safety hazards. These hypotheses will be tested via an experiment where the level of detail of design information is manipulated and errors, safety hazards, and uncertainty are purposely embedded in the test task. The experiment will involve a scale model of an actual construction project. Two hundred craft workers and two hundred undergraduate students will participate in the research experiment to represent expert and novice skill levels in the study conditions. In addition to quantitative analyses, interviews will be conducted with the study participants after each experimental trial to explain the results. This research will have the potential to build new knowledge on what and how design information can be best delivered to future craft workers using emerging technologies to enable efficient, safe, and high-quality construction.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
