Journal of Visualized Experiments · Oct 23, 2026
A Methodological Framework for a Large Language Model Virtual Companion for Older Adults at Risk of Social Isolation
By Yao H
Social isolation is consistently associated with depressive symptoms, loneliness, reduced quality of life, and limited social connectedness in older adults; however, standardized approaches are lacking for developing and evaluating artificial intelligence-based psychosocial interventions. Here, we report an integrated methodology for developing and evaluating a large language model (LLM)-driven virtual companion system that delivers personalized emotional support to older adults at risk of social isolation. The methodology includes: participant recruitment and eligibility screening, system deployment, personalized conversational configuration, emotion-aware interaction, intervention implementation, longitudinal outcome assessment, and statistical evaluation. As a proof-of-concept demonstration of the framework, we conducted a randomized controlled trial involving 160 older adults recruited from community settings and long-term care institutions. Older adults were randomly allocated into one of two groups: the intervention group received an 8-week LLM-driven virtual companion intervention, while the control group participated in routine social activities. We evaluated outcomes at baseline, Week 4, Week 8, and Week 12 using the Geriatric Depression Scale (GDS-15), UCLA Loneliness Scale (UCLA-LS), the World Health Organization Quality of Life-Old Module (WHOQOL-OLD), Lubben Social Network Scale-6 (LSNS-6), and System Usability Scale (SUS). Representative findings showed good system usability (SUS = 78.6 ± 10.3) and favorable changes in depressive symptoms and loneliness, indicating the feasibility and practicality of the proposed methodological framework. The standardized workflow provides a reproducible protocol for the further development and evaluation of LLM-based digital mental health interventions for older adults in both community and long-term care settings.