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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.

 

A social media phenomenon that has received limited research attention is the advent and propagation of viral online challenges. Several of these challenges entail self-harming behavior, which, combined with their viral nature, poses physical and psychological risks for both participants and viewers. The objective of this study is to identify the nature of what people post about the social media challenges that vary in their level of risk. To do so, we conducted a qualitative analysis of three viral social media challenges, the Blue Whale, Tide Pod, and Ice Bucket challenges, based on 180 YouTube videos, 3,607 comments on those YouTube videos, and 450 Twitter posts. We identified common themes across the YouTube videos, comments, and Twitter posts: (1) promoting education and awareness, (2) criticizing the participants, (3) providing detailed information about the participants, (4) giving viewers a tutorial on how to participate, and (5) attempting to understand this seemingly senseless online behavior. We used social norm theory to discuss what leads people to post about the challenges and how posts intended to raise awareness about harmful challenges could potentially create a contagion effect by spreading knowledge about them, thereby increasing participation. Finally, we proposed design implications that could potentially minimize the risks and propagation of harmful social media challenges. 

Critical for the early diagnosis of genetic disorders, a Family Health History (FHx) can be collected in several ways including electronic FHx tools, which aid easy editing and sharing by linking with other information management portals. The user acceptance of such systems is critical, especially among older adults experiencing motor and cognitive issues. This study investigated two types of FHx interfaces, standard and Virtual Conversational Agent (VCA), using 30 young (between 18 and 30) and 24 older participants (over 60). Workload, usability and performance data were collected. Even though participants required less time to complete three of five tasks on the standard interface, the VCA interface performed better in terms of subjective workload and usability. Additionally, 67% of the older adults preferred the VCA interface since it provided context-based guidance during the data collection process. The results from this study have implications for the use of virtual assistants in FHx and other areas of data collection.