The impact of perceived design origin attribution (Human vs AI) on trust and performance in augmented reality assembly tasks among construction professionals

Engineering notes

The findings revealed that user trust is not uniform: participants’ perceptions of design accuracy remained stable regardless of origin, even when they noticed errors, whereas trust in the design’s safety decreased significantly when it was attributed to AI.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

As the construction industry accelerates its digital transformation, it is essential to thoroughly investigate the value proposition of emerging technologies. This study investigated the complex interplay between the perceived origin of a design (AI vs. Human), the information format (AR vs. Paper), user trust, and work performance. The objective was to understand how user trust is affected by the perceived origin of a design and whether it influences user work performance in an AR-assisted environment. To explore this, we conducted a controlled experiment with one hundred practicing industry craft workers performing a full-scale Mechanical, Electrical, and Plumbing (MEP) assembly task. With the use of deception, the perceived origin of the design was manipulated as either AI or human-generated. Participants used traditional paper drawings or one of two AR models to complete the task. We recorded self-reported trust, both before and after the task, as well as work performance factors such as task time, rework, and errors. The findings revealed that user trust is not uniform: participants’ perceptions of design accuracy remained stable regardless of origin, even when they noticed errors, whereas trust in the design’s safety decreased significantly when it was attributed to AI. Regarding performance, the AR interface notably enhanced accuracy by reducing rework, regardless of the design’s perceived origin. Task speed, however, was mainly influenced by the user’s innate spatial cognitive ability. These outcomes suggest that successful integration of AI into construction will not depend just on the capabilities of the algorithms but on the entire sociotechnical system, with the user interface playing a crucial mediating role. Therefore, to unlock the full potential of these technologies, experts must address underlying human factors such as trust and user perception.

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