Conversational Analyses of Older Adults’ Social Engagement with AI-Driven Socially Assistive Robots

Abstract Objectives The digital divide and limited digital literacy hinder technology adoption among older adults in low-resource communities. This study examines how socially assistive robots (SARs) can facilitate social engagement and emotional well-being among older adults by analyzing conversational interactions with the AI-driven Hyodol SAR. Methods Multimodal data—including log-based usage patterns and conversational voice data—were collected through SAR-embedded sensors. Pre- and post-surveys gathered information on demographics and health status. Human-robot conversations were categorized into nine emotional and topical categories and six types of activity participation. To explore user engagement patterns, K-means clustering was applied to identify distinct user personas. Results Among participants, 44.6% engaged in conversations with the SARs, with 30.2% discussing their activity participation. Three personas emerged: Social Butterflies (n = 19, 28.35%) maintained balanced engagement in social and personal activities, with positive emotional exchanges but limited long-term impact on well-being. Lone Wolves (n = 28, 41.79%) had low social engagement, yet showed notable improvements in emotional well-being through conversational interactions with the SAR. Emotional Peacocks (n = 20, 29.85%) displayed high emotional and sensory engagement with the SAR, demonstrating the greatest reduction in loneliness among the three groups. Discussion Findings suggest that SARs helped mitigate social isolation, especially among older adults with limited social engagement. Furthermore, the integration of large language models with SAR technology enables autonomous, dialogue-based AI companions delivering personalized, emotion-sensitive interactions. Future research should explore adaptive AI learning models, ethical considerations in caregiving, and the long-term effects of SAR-facilitated engagement on well-being.