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Güneysu Özgür, A., Yadollahi, E. & Indurkhya, B. (2023). Lessons learned from in the wild child-robot interaction in multiple ecosystems of care and education. In: HAI 2023: Proceedings of the 11th Conference on Human-Agent Interaction. Paper presented at 11th Conference on Human-Agent Interaction, HAI 2023, Gothenburg, Sweden, December 4-7, 2023 (pp. 142-151). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Lessons learned from in the wild child-robot interaction in multiple ecosystems of care and education
2023 (English)In: HAI 2023: Proceedings of the 11th Conference on Human-Agent Interaction, Association for Computing Machinery (ACM), 2023, p. 142-151Conference paper, Published paper (Refereed)
Abstract [en]

We present here some lessons learned from observations and applications of child-robot interaction research in diverse ecosystems such as schools, therapy centers, and hospitals, where the interaction was facilitated in real-world circumstances rather than lab settings. Specifically, we use observational results from our reflections on multiple child-robot interaction practices in the wild conducted over a 9-year research period. Using these exploratory studies, we outline some general design considerations and adaptation guidelines for improving the design and implementation of robotic systems in healthcare and education that might lead to more practical, feasible, and ethically sustainable results.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Keywords
adaptive systems, gamification, observational studies, special education
National Category
Robotics
Identifiers
urn:nbn:se:umu:diva-229136 (URN)10.1145/3623809.3623855 (DOI)001148034200019 ()2-s2.0-85180126404 (Scopus ID)979-8-4007-0824-4 (ISBN)
Conference
11th Conference on Human-Agent Interaction, HAI 2023, Gothenburg, Sweden, December 4-7, 2023
Available from: 2026-01-21 Created: 2026-01-21 Last updated: 2026-01-21Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7091-0104

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