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Personalized robot tutoring using the Assistive Tutor POMDP (AT-POMDP)

AUTHORs:

Aditi Ramachandran,

Sarah Strohkorb Sebo,

Brian ScassellatiAuthors Info & Claims

AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

Article No.: 987, Pages 8050 - 8057

https://doi.org/10.1609/aaai.v33i01.33018050

Published: 27 January 2019 Publication History

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Abstract

Selecting appropriate tutoring help actions that account for both a student's content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the AT-POMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student's mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n = 28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students.

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Cited By

Natarajan MXue Cvan Waveren SFeigh KGombolay MDastani MSichman JAlechina NDignum V(2024)Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian AdaptationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663005(1454-1462)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663005
Ho HHubbard EMutlu B(2024)"It's Not a Replacement:" Enabling Parent-Robot Collaboration to Support In-Home Learning Experiences of Young ChildrenProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642806(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642806
Ng SLin TLi YSebo SGrollman DBroadbent EJu WSoh HWilliams T(2024)Role-Playing with Robot Characters: Increasing User Engagement through Narrative and Gameplay AgencyProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634941(522-532)Online publication date: 11-Mar-2024
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Index Terms

Personalized robot tutoring using the Assistive Tutor POMDP (AT-POMDP)
1. Applied computing
  1. Education
2. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

January 2019

10088 pages

ISBN:978-1-57735-809-1

Copyright © 2019 Association for the Advancement of Artificial Intelligence.

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AAAI Press

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Published: 27 January 2019

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Cited By

Natarajan MXue Cvan Waveren SFeigh KGombolay MDastani MSichman JAlechina NDignum V(2024)Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian AdaptationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663005(1454-1462)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663005
Ho HHubbard EMutlu B(2024)"It's Not a Replacement:" Enabling Parent-Robot Collaboration to Support In-Home Learning Experiences of Young ChildrenProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642806(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642806
Ng SLin TLi YSebo SGrollman DBroadbent EJu WSoh HWilliams T(2024)Role-Playing with Robot Characters: Increasing User Engagement through Narrative and Gameplay AgencyProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634941(522-532)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610977.3634941
Ho HWhite NHubbard EMutlu B(2023)Designing Parent-child-robot Interactions to Facilitate In-Home Parental Math Talk with Young ChildrenProceedings of the 22nd Annual ACM Interaction Design and Children Conference10.1145/3585088.3589358(355-366)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3585088.3589358
Salomons NPineda KAdéjàre AScassellati BSakamoto DWeiss AHiatt LShiomi M(2022)"We Make a Great Team!": Adults with Low Prior Domain Knowledge Learn more from a Peer Robot than a Tutor RobotProceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction10.5555/3523760.3523787(176-184)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.5555/3523760.3523787
Cagiltay BMichaelis JSebo SMutlu B(2022)Exploring Children’s Preferences for Taking Care of a Social RobotProceedings of the 21st Annual ACM Interaction Design and Children Conference10.1145/3501712.3529721(382-388)Online publication date: 27-Jun-2022
https://dl.acm.org/doi/10.1145/3501712.3529721
Villanueva ALiu ZZhu ZDu XHuang JPeppler KRamani KKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)RobotAR: An Augmented Reality Compatible Teleconsulting Robotics Toolkit for Augmented Makerspace ExperiencesProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445726(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445726

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