Shaping Relatable Robots: A Child-Centered Approach to Social Personalization

Elena Malnatsky, Shenghui Wang, Koen Hindriks, Mike Ligthart

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

While social robots hold signifcant potential in education, not all children fnd their interaction with a robot relatable. We present a child-centered research approach that actively involves children in shaping personalized interaction content. We applied this method in a user study (n = 102, 8-13 y.o) where we designed robot humor that was tailored to diferent age groups. Results indicated that children found age-personalized humor more amusing and felt a
stronger afnity with it, both personally and at the group level. Our forthcoming longitudinal study will focus on enhancing children’s relatedness to the robot and a book, aiming to stimulate reading motivation. We plan to investigate how generative AI can efficiently scale up both co-design and content creation steps.
Original languageEnglish
Title of host publicationHRI'24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages127-129
Number of pages3
ISBN (Electronic)978-8-4007-0323-2
DOIs
Publication statusPublished - 11 Mar 2024
Event19th ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024 - University Memorial Center, Boulder, United States
Duration: 11 Mar 202415 Mar 2024
Conference number: 19

Conference

Conference19th ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024
Abbreviated titleHRI 2024
Country/TerritoryUnited States
CityBoulder
Period11/03/2415/03/24

Keywords

  • 2024 OA procedure

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