2025/07/18 by Xianhao Carton Liu, Liu, Xianhao Carton, Difan Jia +9 · 1 citation
Computer Science · Engineering · #Augmented Reality Applications #Augmented reality #Data visualization #Empirical research #Information visualization #Perception #Spatial Cognition and Navigation #Virtual Reality Applications and Impacts #Visual analytics #Visualization #Workload
paper · pdf · open access · doi:10.1145/3772318.3790710
openalex created_date 2025/10/10 · openalex publication_date 2026/04/13 · openalex updated_date 2026/08/05
Artificial Intelligence (AI) and indoor sensing increasingly support decision-making in spatial environments. However, traditional visualization methods impose a substantial mental workload when viewers translate this digital information into real-world spaces, leading to inappropriate reliance on AI. Embedded visualizations in Augmented Reality (AR), by integrating information into physical environments, may reduce this workload and foster more appropriate reliance on AI. To assess this, we conducted an empirical study (N = 32) comparing an AR embedded visualization (X-ray) and 2D Minimap in AI-assisted, time-critical spatial target selection tasks. Surprisingly, evidence shows that the embedded visualization led to greater inappropriate reliance on AI, primarily as over-reliance, due to factors like perceptual challenges, visual proximity illusions, and highly realistic visual representations. Nonetheless, the embedded visualization showed benefits in spatial mapping. We conclude by discussing empirical insights, design implications, and directions for future research on human-AI collaborative decision in AR.