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dc.contributor.authorKim, Won
dc.contributor.authorSeong, Minwoo
dc.contributor.authorDelPreto, Joseph
dc.contributor.authorMatusik, Wojciech
dc.contributor.authorRus, Daniela
dc.contributor.authorKim, SeungJun
dc.date.accessioned2024-11-21T15:40:09Z
dc.date.available2024-11-21T15:40:09Z
dc.date.issued2024-10-05
dc.identifier.isbn979-8-4007-1058-2
dc.identifier.urihttps://hdl.handle.net/1721.1/157623
dc.descriptionUbiComp Companion ’24, October 5–9, 2024, Melbourne, VIC, Australiaen_US
dc.description.abstractActive engagement where children with autism spectrum disorder (ASD) are involved (e.g., educational and social activities) plays a crucial role in enhancing their cognitive, motor, and social development. This offers opportunities to enhance overall development, including learning abilities, physical coordination, and social interactions. Indirect methods, leveraging sensors and artificial intelligence (AI), have exhibited potential for enhancing engagement predictions but have been primarily focused within specific fields, resulting in a gap that leads to limited generalizability of ASD studies. This gap, due to small ASD sample sizes, presents a significant challenge as the annual ASD population increases, highlighting the need for practical and applicable research solutions, especially for general learning. In this work, we conducted expert interviews to explore the potential application areas of AI-infused systems that provide three levels of engagement status for children with ASD, ranging from "not engaged and out of control" to "highly engaged." Interviews with special educators revealed five key application areas for AI-driven engagement recognition: social skills training, stereotyped behavior modification, support for leisure activities, effective tutoring, and independent daily living skills. These findings highlight the potential of adaptive AI interventions in improving educational and daily outcomes, advocating for expanded applications for children with ASD.en_US
dc.publisherACM|Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computingen_US
dc.relation.isversionofhttps://doi.org/10.1145/3675094.3678376en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleExploring Potential Application Areas of Artificial Intelligence-Infused System for Engagement Recognition: Insights from Special Education Expertsen_US
dc.typeArticleen_US
dc.identifier.citationKim, Won, Seong, Minwoo, DelPreto, Joseph, Matusik, Wojciech, Rus, Daniela et al. 2024. "Exploring Potential Application Areas of Artificial Intelligence-Infused System for Engagement Recognition: Insights from Special Education Experts."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2024-11-01T07:53:00Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2024-11-01T07:53:01Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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