research-article

Modeling Adaptive Expression of Robot Learning Engagement and Exploring Its Effects on Human Teachers

Authors:

Xiaojuan MaAuthors Info & Claims

ACM Transactions on Computer-Human Interaction, Volume 30, Issue 5

Article No.: 70, Pages 1 - 48

https://doi.org/10.1145/3571813

Published: 23 September 2023 Publication History

Abstract

Robot Learning from Demonstration (RLfD) allows non-expert users to teach a robot new skills or tasks directly through demonstrations. Although modeled after human–human learning and teaching, existing RLfD methods make robots act as passive observers without the feedback of their learning statuses in the demonstration gathering stage. To facilitate a more transparent teaching process, we propose two mechanisms of Learning Engagement, Z2O-Mode and D2O-Mode, to dynamically adapt robots’ attentional and behavioral engagement expressions to their actual learning status. Through an online user experiment with 48 participants, we find that, compared with two baselines, the two kinds of Learning Engagement can lead to users’ more accurate mental models of the robot’s learning progress, more positive perceptions of the robot, and better teaching experience. Finally, we provide implications for leveraging engagement expression to facilitate transparent human-AI (robot) communication based on our key findings.

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Cited By

Yu JChauhan DIqbal RYeoh E(2024)Mapping academic perspectives on AI in education: trends, challenges, and sentiments in educational research (2018–2024)Educational technology research and development10.1007/s11423-024-10425-2Online publication date: 30-Sep-2024
https://doi.org/10.1007/s11423-024-10425-2

Index Terms

Modeling Adaptive Expression of Robot Learning Engagement and Exploring Its Effects on Human Teachers
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
  2. Interaction design
    1. Empirical studies in interaction design

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cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 30, Issue 5

October 2023

593 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/3623487

Editors:
Kristina Höök
KTH Royal Institute of Technology, Sweden
,
Kasper Hornbæk
University of Copenhagen, Denmark

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Publication History

Published: 23 September 2023

Online AM: 19 November 2022

Accepted: 05 August 2022

Revised: 31 May 2022

Received: 30 June 2021

Published in TOCHI Volume 30, Issue 5

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Yu JChauhan DIqbal RYeoh E(2024)Mapping academic perspectives on AI in education: trends, challenges, and sentiments in educational research (2018–2024)Educational technology research and development10.1007/s11423-024-10425-2Online publication date: 30-Sep-2024
https://doi.org/10.1007/s11423-024-10425-2

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