{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T15:06:34Z","timestamp":1773846394275,"version":"3.50.1"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"name":"National Science Foundation","award":["1840088"],"award-info":[{"award-number":["1840088"]}]},{"name":"Johns Hopkins University Institute for Assured Autonomy, and Malone Center for Engineering in Healthcare."}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Access. Comput."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Our work addresses the challenges older adults face with commercial Voice Assistants (VAs), notably in conversation breakdowns and error handling. Traditional methods of collecting user experiences\u2014usage logs and\n                    <jats:italic toggle=\"yes\">post hoc<\/jats:italic>\n                    interviews\u2014do not fully capture the intricacies of older adults\u2019 interactions with VAs, particularly regarding their reactions to errors. To bridge this gap, we equipped 15 older adults\u2019 homes with smart speakers integrated with custom audio recorders to collect \u201cin-the-wild\u201d audio interaction data for detailed error analysis. Recognizing the growing use of Large Language Models (LLMs) to enhance the capabilities of VAs, our study also explored how this integration of LLMs changes older adults\u2019 interaction dynamics, specifically during errors. Midway through our study, we deployed ChatGPT-powered VA to investigate its efficacy for older adults. Our research suggests that while technical improvements\u2014such as leveraging vocal and verbal responses combined with LLMs\u2019 contextual capabilities\u2014can enhance error prevention and management in VAs, interaction-level challenges still remain, particularly those unique to older adults. We propose design considerations to better align future VAs with older adults\u2019 expectations and lived experiences.\n                  <\/jats:p>","DOI":"10.1145\/3796236","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:37:48Z","timestamp":1770395868000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Situated Understanding of Errors in Older Adults\u2019 Interactions with Voice Assistants: A Month-Long, In-Home Study"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5044-1494","authenticated-orcid":false,"given":"Amama","family":"Mahmood","sequence":"first","affiliation":[{"name":"Computer Science, Johns Hopkins University, Baltimore, Maryland, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7264-2489","authenticated-orcid":false,"given":"Junxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Robotics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6838-3701","authenticated-orcid":false,"given":"Chien-Ming","family":"Huang","sequence":"additional","affiliation":[{"name":"Computer Science, Johns Hopkins University, Baltimore, Maryland, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/978-3-030-74605-6_17","volume-title":"Proceedings of the 21st Congress of the International Ergonomics Association (IEA \u201921)","author":"Angelini Leonardo","year":"2021","unstructured":"Leonardo Angelini, Maurizio Caon, Emmanuel Michielan, Omar Abou Khaled, and Elena Mugellini. 2021. 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