Designing a Generative AI Companion for Seniors

Loneliness and social isolation among older adults have emerged as urgent public health challenges, with significant physical, emotional, and economic consequences. In response, this study explores whether emotionally intelligent AI agents can support older adults through sustained, meaningful interactions. We present Seva, a voice-based, GenAI-powered conversational companion designed to reduce loneliness by simulating empathetic, personalized dialogue. The study follows the Elaborated Action Design Research (EADR) methodology, progressing through diagnosis, design, implementation, and evolution phases. This informed the development of a mobile-based app integrating large language models, voice recognition, and personalization modules. Early findings suggest that features such as empathetic tone and memory-aware responses were positively received. This research contributes to the ongoing discourse on AI-enabled companionship and provides early-stage insights into the development of emotionally attuned conversational agents for aging populations.

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Publication Details

Journal
Journal of the Association for Information Systems
Published
2025-08-15
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
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article

Designing a Generative AI Companion for Seniors

Journal of the Association for Information Systems
Digital Mental Health Interventions
article

Designing a Generative AI Companion for Seniors

article en

Abstract

Loneliness and social isolation among older adults have emerged as urgent public health challenges, with significant physical, emotional, and economic consequences. In response, this study explores whether emotionally intelligent AI agents can support older adults through sustained, meaningful interactions. We present Seva, a voice-based, GenAI-powered conversational companion designed to reduce loneliness by simulating empathetic, personalized dialogue. The study follows the Elaborated Action Design Research (EADR) methodology, progressing through diagnosis, design, implementation, and evolution phases. This informed the development of a mobile-based app integrating large language models, voice recognition, and personalization modules. Early findings suggest that features such as empathetic tone and memory-aware responses were positively received. This research contributes to the ongoing discourse on AI-enabled companionship and provides early-stage insights into the development of emotionally attuned conversational agents for aging populations.

Journal of the Association for Information Systems
University of North Carolina at Greensboro (US)
Reduced inequalities
Openalex Percentile: Top 43%
Digital Mental Health Interventions
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