Jing Qian

Chenyi Li, Guande Wu, Gromit Yeuk-Yin Chan et al.

Augmented Reality (AR) assistance is increasingly used for supporting users with physical tasks like assembly and cooking. However, most systems rely on reactive responses triggered by user input, overlooking rich contextual and user-specific information. To address this, we present Satori, a novel AR system that proactively guides users by modeling both -- their mental states and environmental contexts. Satori integrates the Belief-Desire-Intention (BDI) framework with the state-of-the-art multi-modal large language model (LLM) to deliver contextually appropriate guidance. Our system is designed based on two formative studies involving twelve experts. We evaluated the system with a sixteen within-subject study and found that Satori matches the performance of designer-created Wizard-of-Oz (WoZ) systems, without manual configurations or heuristics, thereby improving generalizability, reusability, and expanding the potential of AR assistance.

Duotun Wang, Jennifer Healey, Jing Qian et al.

We present the result of a pilot study measuring child engagement with the Lets Make A Story system, a novel mixed reality, MR, collaborative storytelling system designed for grandparents and grandchildren. We compare our MR experience against an equivalent paper story experience. The goal of our pilot was to test the system with actual child users and assess the goodness of using metrics of time, user generated story content and facial expression analysis as metrics of child engagement. We find that multiple confounding variables make these metrics problematic including attribution of engagement time, spontaneous non-story related conversation and having the childs full forward face continuously in view during the story. We present our platform and experiences and our finding that the strongest metric was user comments in the post-experiential interview.