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

TL;DR

This study explores how AI-driven robots help older adults in low-resource communities reduce loneliness and improve emotional well-being through social engagement.

Contribution

The study introduces a novel approach using AI-driven socially assistive robots to analyze conversational and engagement patterns in older adults.

Findings

44.6% of participants engaged in conversations with the AI-driven robot, with 30.2% discussing activity participation.

Three distinct user personas emerged, each showing different levels of social and emotional engagement with the robot.

The robots helped reduce loneliness, especially among older adults with limited social interaction.

Abstract

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.

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.

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.

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.