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AdapTutAR: An Adaptive Tutoring System for Machine Tasks in Augmented Reality

Authors:

Maitreya Sreeram,

Karthik Ramani,

Alexander J. QuinnAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 417, Pages 1 - 15

https://doi.org/10.1145/3411764.3445283

Published: 07 May 2021 Publication History

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Abstract

Modern manufacturing processes are in a state of flux, as they adapt to increasing demand for flexible and self-configuring production. This poses challenges for training workers to rapidly master new machine operations and processes, i.e. machine tasks. Conventional in-person training is effective but requires time and effort of experts for each worker trained and not scalable. Recorded tutorials, such as video-based or augmented reality (AR), permit more efficient scaling. However, unlike in-person tutoring, existing recorded tutorials lack the ability to adapt to workers’ diverse experiences and learning behaviors. We present AdapTutAR, an adaptive task tutoring system that enables experts to record machine task tutorials via embodied demonstration and train learners with different AR tutoring contents adapting to each user’s characteristics. The adaptation is achieved by continually monitoring learners’ tutorial-following status and adjusting the tutoring content on-the-fly and in-situ. The results of our user study evaluation have demonstrated that our adaptive system is more effective and preferable than the non-adaptive one.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

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Yoshifumi Kitamura
Tohoku University, Japan
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Aaron Quigley
University of New South Wales, Australia
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Katherine Isbister
University of California Santa Cruz, USA
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Takeo Igarashi
The University of Tokyo, Japan
,
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Pernille Bjørn
University of Copenhagen, Denmark
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Steven Drucker
Microsoft Research, USA

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Tran O'Leary JRamesh TZhang OPeek N(2024)Tandem: Reproducible Digital Fabrication Workflows as Multimodal ProgramsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642751(1-16)Online publication date: 11-May-2024
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