dc.contributor.author | Kim, Won | |
dc.contributor.author | Seong, Minwoo | |
dc.contributor.author | DelPreto, Joseph | |
dc.contributor.author | Matusik, Wojciech | |
dc.contributor.author | Rus, Daniela | |
dc.contributor.author | Kim, SeungJun | |
dc.date.accessioned | 2024-11-21T15:40:09Z | |
dc.date.available | 2024-11-21T15:40:09Z | |
dc.date.issued | 2024-10-05 | |
dc.identifier.isbn | 979-8-4007-1058-2 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/157623 | |
dc.description | UbiComp Companion ’24, October 5–9, 2024, Melbourne, VIC, Australia | en_US |
dc.description.abstract | Active 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.publisher | ACM|Companion of the 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing | en_US |
dc.relation.isversionof | https://doi.org/10.1145/3675094.3678376 | en_US |
dc.rights | Creative Commons Attribution | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Association for Computing Machinery | en_US |
dc.title | Exploring Potential Application Areas of Artificial Intelligence-Infused System for Engagement Recognition: Insights from Special Education Experts | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Kim, 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.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.identifier.mitlicense | PUBLISHER_CC | |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2024-11-01T07:53:00Z | |
dc.language.rfc3066 | en | |
dc.rights.holder | The author(s) | |
dspace.date.submission | 2024-11-01T07:53:01Z | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |