It was found that interaction intensity, companionship use, and self-disclosure all showed substantial continuity over time, consistent with the longitudinal engagement pathway, and sustained social engagement across these dimensions was consistently associated with lower well-being.
Abstract
AI chatbots are increasingly used for companionship, emotional support, and personal self-disclosure; however, how social engagement with these systems unfolds over time and shapes users'well-being remains unclear. To address this, we conducted a two-wave longitudinal study of CharacterAI users, surveying 1,182 participants at baseline and 439 after a mean follow-up of 12 months. We examined how social engagement with AI companions evolves and how these longitudinal engagement patterns may influence well-being through two hypothesized pathways: sustained social engagement over time and the displacement of human social interaction. We found that interaction intensity, companionship use, and self-disclosure all showed substantial continuity over time. Greater interaction intensity at baseline predicted greater subsequent interaction intensity, companionship use, and self-disclosure. Consistent with the longitudinal engagement pathway, sustained social engagement across these dimensions was consistently associated with lower well-being. Results further support the social displacement pathway, indicating that these links were mainly explained by lower in-person social interaction. These findings highlight the importance of designing AI companions that support human social relationships without displacing them.
These findings provide early field evidence that autonomous, LLM-driven conversational robots can maintain emotionally supportive dialogue with older adults, fostering emotional engagement, trust and sustained interaction over time, mechanisms that may contribute to reducing perceived social isolation.
María J. Pinto-Bernal, Kato Boels, Tony Belpaeme· ACM Transactions on Human-Ro...· 0 citations
Conversational artificial intelligence (AI) is increasingly used for emotional support, reflection, and consequential decision-making. Although conversational continuity may improve responsiveness, sustained interaction also exposes AI systems to their own prior relational language, potentially shaping how they res...
Xiao Wei, Xiao-Yi Chen, Wangahe Jin et al.· Frontiers in Psychiatry· 0 citations
These findings portray relational AI as a heterogeneous socio-emotional context that may partly mirror users'states and traits, warranting individualized, adaptive safeguards.
Lu Chen, Feng-Hua Tang, Jia-Yu Zhao et al.· 0 citations
OBJECTIVES
Intergenerational engagement-here referring to time spent with younger family members-can promote health and happiness. Across adulthood, and particularly in later life, intergenerational engagement represents a potentially meaningful form of social involvement. Using intensive longitudinal methods, we inves...
Claire M. Growney, T. English· The journals of gerontology....· 0 citations
These findings show that companion-AI safety must address relationships over time through user-controlled memory, privacy, relational boundaries, and healthy disengagement.
M. Namvarpour, Tyler A. Chang, Afsaneh Razi· 0 citations
PAIR, a theory-based emotion-regulation companion, was deployed with 19 participants for 14 days and linked memory updates and retained corrections to cross-session personalization, informing future emotional support tools that adapt to evolving needs, learn from prior outcomes, and preserve user control over memory.
Kexin Quan, Zi-Jian Ding, Jia-Ye Yong et al.· 0 citations
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