# Pathways of long-term AI virtual companion app use on users' attachment emotions: a case study of Chinese users

## TL;DR

This study explores how frequent use of AI virtual companions in China affects users' emotional attachment and real-world social behavior.

## Contribution

It applies attachment theory to human–AI relationships and identifies self-concept clarity as a key mediator in social engagement.

## Key findings

- Frequent use of AI companions increases emotional attachment (β = 0.44).
- Emotional attachment reduces loneliness and boosts wellbeing and self-concept clarity.
- Self-concept clarity most strongly links emotional attachment to real-world social engagement.

## Abstract

Although algorithmic companionship is becoming an increasingly integral part of daily life, evidence remains fragmented regarding whether AI virtual companions can become stable attachment figures, and how such attachments influence users' psychological states and offline social behaviors. Understanding these dynamics is particularly crucial in rapidly digitizing environments such as China, where mobile AI virtual companion applications are widely adopted.

This study takes a mixed-methods approach based on attachment theory. An initial systematic literature review (SLR) was conducted to clarify the research variables and their theoretical foundations. Subsequently, semi-structured interviews were conducted with 10 users who had at least 6 months' experience of continuous usage to refine variable definitions and measurement items. Finally, a cross-sectional questionnaire survey was conducted in mainland China (N = 612). Structural equation modeling (SEM) was used to analyze the associations between usage frequency, emotional attachment, loneliness, subjective wellbeing, self-concept clarity, and real-world social engagement. After assessing the psychometric properties via confirmatory factor analysis (CFA) and reliability indices, the mediating pathways of these associations were examined.

The frequency of use positively correlates with emotional attachment to AI virtual companions (β = 0.44). Attachment negatively correlates with loneliness (β = −0.32) and positively with subjective wellbeing (β = 0.41) and self-concept clarity (β = 0.51). Of the three psychological pathways, those associated with loneliness, wellbeing, and self-concept clarity were found to be linked to higher levels of real-world social engagement. The indirect association via self-concept clarity was found to be the most significant. The model demonstrated an overall good fit [comparative fit index (CFI) = 0.97; root mean square error of approximation (RMSEA) = 0.04].

This study applies attachment theory to the domain of human–AI relationships, using Chinese users as a case study. It constructs a model that links ‘usage frequency, emotional attachment, psychological state, and real-world social engagement'. Self-concept clarity plays a vital role in bridging the gap between emotional attachment and real-world social engagement. Design implications include enhancing continuity features, contextual memory, and self-expression design, with the aim of fostering healthier psychological and social outcomes in AI virtual companion-related attachment.