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

Liu, Ting, Lo, Ting-Yun, Wen, Kuo-Hsun, Sun, Yue, Wei, Zheng-Qi·2026·Frontiers in Psychology
DOI 10.3389/fpsyg.2025.1687686PMC PMC12833267
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Abstract

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 behaviors. Understanding these dynamics is particularly crucial in rapidly digitizing 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 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. 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 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]. 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.

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  • The robustness analysis with attachment anxiety indicates that the moderation pattern is not limited to loneliness alone but may reflect a broader vulnerability among users with heightened relational insecurity.

    “The robustness analysis with attachment anxiety further indicates that the moderation pattern is not limited to loneliness alone, but may reflect a broader vulnerability among users with heightened relational insecurity (Ciriello et al., 2026; Knox et al., 2025; Liu et al., 2026).”

    From When loneliness amplifies the risk of problematic emotional dependence: perceived anthropomorphism and basic psychological need satisfaction in AI companion use (2026) · cites this paper as Liu et al., 2026

  • Most data sources for research on AI companion relationships are based on capturing users' responses to researcher-constructed prompts via surveys, interviews, and experimental manipulations.

    “However, most of the data sources for these efforts are based on capturing users' responses to researcher-constructed prompts via surveys (Zhang et al., 2025), interviews (Liu et al., 2026), and experimental manipulations (Pataranutaporn et al., 2023).”

    From Me and My Bot: What Users Talk About in AI Companion Communities on Reddit (2026) · cites this paper as Liu et al., 2026

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Metadata

Authors
Liu, T., Lo, T.-Y., Wen, K.-H., Sun, Y., & Wei, Z.-Q.
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2026-01-12
Volume
16
ISSN
1664-1078
DOI
10.3389/fpsyg.2025.1687686
PMC
PMC12833267
Cited by
13
References
14
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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