Work

Understanding problematic AI companion use through escapism and resilience

Year: 2026

Type: article

Abstract: As large language models (LLMs) become more sophisticated and relational, many users now engage with them in ways that resemble friendship, romance, or ongoing emotional support. While these interactions can be positive, emerging evidence suggests that a subset of users experience difficulties regulating their engagement, leading to patterns better characterized as problematic, dysregulated, or maladaptive use. Guided by Compensatory Internet Use Theory and the I-PACE model, this study examines a risk-buffer framework in which escapism is associated with greater problematic AI-companion use and resilience weakens that association. Results indicate that escapism is associated with problematic use of AI characters and resilience significantly attenuates this association. Together, these findings point to a pattern in which escapism is linked to greater problematic use, whereas resilience functions as a protective psychological resource associated with a weaker escapism–problematic use relationship.

Source: Computers in Human Behavior Artificial Humans

DOI: 10.1016/j.chbah.2026.100308

Author: Justin W. Carter

Institution: University of North Alabama

Language: English

Topic: Social Robot Interaction and HRI

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,540