Beyond (J)ust a (R)ather (V)ery (I)ntelligent (S)ystem: Can You Outdo Ironman Troubleshooting Complex Technical Problems?

AI enhances the adaptability and versatility of augmented reality. AI is expected to revolutionize existing methods of augmented processing and natural voice interactions. However, most studies predominantly evaluate performance improvement against traditional face-to-face collaboration and problem solving in simple tasks. Consequently, there is a paucity of research that systematically identifies the key augmented reality designs for human-AI collaboration. More critically, our understanding of individuals’ psychological processes in such collaborations remains fragmented. Drawing from the gestalt principle and processing fluency literature, this study considers two specific features facilitating human-AI collaborative troubleshooting through augmented reality: associative linkage and referential indication. Associative linkage, which is often implemented through corresponding numbers, icons, and content structures, has been broadly applied to help individuals identify and comprehend multiple pieces of information. Referential indication has been broadly used to emphasize group or focal information. To test our hypotheses, we employed neuroscience techniques in a laboratory experiment.