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search.filters.applied.f.advisor: Mynatt, Elizabeth D. × search.filters.applied.f.isOrgUnitOfPublicationTitle: School of Interactive Computing × search.filters.applied.f.isSeriesOfPublicationTitle: Doctor of Philosophy with a Major in Computer Science ×

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“I Can Help with That” - Designing Flexible, Personalized and Proactive Conversational Agents for Older Adults

2024-04-27 , Zubatiy, Tamara

Observing how older adults interact with commercial conversational agents reveals limitations in existing CA systems and contributes to design guidelines for future systems to overcome these limitations. Despite widespread adoption and study of pre-large-language model conversational agents (CAs), they are still error prone and frustrating due to lack of proactivity, personalization over time and inflexibility of input. Importantly, while they stand to greatly benefit older adults, through on-demand support that doesn’t require interacting with a touch screen, limited research has been done to explore how older adults use CAs over time at home. To contribute to this opportunity at the intersection of technology and health, my research combines user insights gleaned from multiple longitudinal deployments of commercial CAs into the homes of older adults, a battery of qualitative & quantitative statistical analyses, and an understanding of optimal training approaches designed specifically for older adults experiencing age related decline. My research contextualizes the usage patterns & frustrations of older adults into existing literature on CA usage by other populations, reveals clear impacts of cognitive status on CA usage by older adults and points to the power of training to increase CA interaction rigor. It also highlights three specific limitations in existing commercial CAs and points to tradeoffs that future systems, likely powered in part by large language models, will need to address in order to overcome them. This work is interwoven with a privacy-focused component including privacy impact assessments (PIA) two existing and one developing CA systems. Each PIA weighs the impact of these three tradeoffs on future CAs and provides actionable recommendations for privacy preserving future systems that still deliver on the personalized and proactive promise of future CAs.

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