Gerontologist. 2026 Feb 15:gnag009. doi: 10.1093/geront/gnag009. Online ahead of print.
ABSTRACT
BACKGROUND AND OBJECTIVES: The digital divide and limited AI literacy pose significant barriers to technology adoption among older adults in low-resource communities. This study investigates the potential of socially assistive robots (SARs) to promote social engagement and psychosocial well-being by analyzing interactions with the AI-driven Hyodol SAR.
RESEARCH DESIGN AND METHODS: Multimodal data-including log-based usage patterns and voice recordings-were collected via SAR-embedded sensors. Pre- and post-intervention surveys provided demographic and health information. Human-robot conversations were classified into nine emotional and topical categories, along with six types of activity participation. K-means clustering was employed to identify distinct user personas reflecting engagement patterns.
RESULTS: Of the participants, 44.6% engaged in conversation with the SAR, and 30.2% discussed activity participation. Three user personas emerged: Social Engagers (28.35%) balanced social and personal interactions with positive emotional tone; Independent Reflectors (41.79%) showed high conversational engagement; Emotionally Expressive Users (29.85%) demonstrated the highest overall SAR usage, including tactile and content-based interactions. While some clusters exhibited numerical reductions in loneliness and depression, these changes did not reach statistical significance.
DISCUSSION AND IMPLICATIONS: These findings suggest that SARs can complement caregiving services for older adults in low-resource communities. By integrating narrative data with quantitative survey responses and usage logs, this study advances methodological approaches in AI-driven gerontological research. The results highlight opportunities for persona-based customization, AI-adaptive learning, and emotion-informed care in future SAR development.
PMID:41692979 | DOI:10.1093/geront/gnag009
