An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks

@inproceedings{10.1145/3313831.3376688,
author = {Cao, Yuanzhi and Qian, Xun and Wang, Tianyi and Lee, Rachel and Huo, Ke and Ramani, Karthik},
title = {An Exploratory Study of Augmented Reality Presence for Tutoring Machine Tasks},
year = {2020},
isbn = {9781450367080},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3313831.3376688},
doi = {10.1145/3313831.3376688},
abstract = {Machine tasks in workshops or factories are often a compound sequence of local, spatial, and body-coordinated human-machine interactions. Prior works have shown the merits of video-based and augmented reality (AR) tutoring systems for local tasks. However, due to the lack of a bodily representation of the tutor, they are not as effective for spatial and body-coordinated interactions. We propose avatars as an additional tutor representation to the existing AR instructions. In order to understand the design space of tutoring presence for machine tasks, we conduct a comparative study with 32 users. We aim to explore the strengths/limitations of the following four tutor options: video, non-avatar-AR, half-body+AR, and full-body+AR. The results show that users prefer the half-body+AR overall, especially for the spatial interactions. They have a preference for the full-body+AR for the body-coordinated interactions and the non-avatar-AR for the local interactions. We further discuss and summarize design recommendations and insights for future machine task tutoring systems.},
booktitle = {Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems},
pages = {1–13},
numpages = {13},
keywords = {augmented reality, avatar tutor, exploratory study, machine task, tutoring system design},
location = {Honolulu, HI, USA},
series = {CHI '20}
}