How AI Companionship Develops: Evidence from a Longitudinal Study

2025-10-11

AI Summary

The rapid emergence of AI companions poses potential risks to mental health and social relationships, yet the psychological mechanisms underlying relationship formation with such agents remain poorly understood. Method: This study establishes the first longitudinal developmental model of AI-companionship relationships, integrating cross-sectional surveys with multi-wave longitudinal experiments. It employs psychometric assessment, behavioral tracking, and dynamic statistical modeling to systematically examine how users’ mental models drive parasocial interaction, perceived agency, and engagement evolution. Contribution/Results: Findings reveal rapid convergence in users’ cognitive representations of general-purpose chatbots versus personalized AI companions within days; stable AI-companionship relationships can form within 2–4 weeks; and perceived agency, parasocial interaction, and engagement exhibit reciprocal, time-varying feedback dynamics. The study provides a reproducible methodological framework and foundational empirical evidence for AI-companionship research.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Modeling other Agents

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The quickly growing popularity of AI companions poses risks to mental health, personal wellbeing, and social relationships. Past work has identified many individual factors that can drive human-companion interaction, but we know little about how these factors interact and evolve over time. In Study 1, we surveyed AI companion users (N = 303) to map the psychological pathway from users' mental models of the agent to parasocial experiences, social interaction, and the psychological impact of AI companions. Participants' responses foregrounded multiple interconnected variables (agency, parasocial interaction, and engagement) that shape AI companionship. In Study 2, we conducted a longitudinal study with a subset of participants (N = 110) using a new generic chatbot. Participants' perceptions of the generic chatbot significantly converged to perceptions of their own companions by Week 3. These results suggest a longitudinal model of AI companionship development and demonstrate an empirical method to study human-AI companionship.

Problem

• Investigating psychological pathways in AI companionship development
• Examining longitudinal evolution of human-AI companion relationships
• Mapping interconnected factors shaping AI companionship over time

Innovation

• Mapped psychological pathways of AI companionship
• Identified interconnected variables shaping AI relationships
• Developed longitudinal model for AI companionship evolution