Pathways of Long-Term AI Virtual Companion App Use on Users' Attachment Emotions: A Case Study of Chinese Users

Ting Liu^1*Ting-Yun Lo¹Kuo-Hsun Wen^2*Yue Sun¹Zheng-Qi Wei¹

• ¹Fujian University of Technology, Fuzhou, China
• ²City University of Macau, Taipa, Macao, SAR China

Background: 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 behaviours. Understanding these dynamics is particularly crucial in rapidly digitising environments such as China, where mobile AI virtual companion applications are widely adopted. Methods: 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 ten users who had at least six 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 modelling (SEM) was used to analyse the associations between usage frequency, emotional attachment, loneliness, subjective well-being, 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. Results: 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 well-being (β ≈0.41) and self-concept clarity (β ≈0.51). Of the three psychological pathways, those associated with loneliness, well-being 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 (CFI ≈0.97; RMSEA ≈0.04). Conclusion: 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.