AI-Mediated Mental Health Support: The Role of Attachment Orientation and Psychological Distress

Abstract

General-purpose generative AI (GenAI) models are increasingly serving as quasi-social agents, representing a novel form of informal AI-mediated help-seeking. This study examined associations between adult attachment orientations (ECR-RS), psychological distress (DASS-21), and the use of GenAI for mental health support. In a nationally diverse sample of 584 adults who utilize GenAI tools, 29.3% of users reported turning to these models for emotional support or coping advice, with prevalence rising to 47.0% among those experiencing severe distress. Hierarchical logistic regression revealed that psychological distress was the primary predictor of use. While attachment anxiety was initially associated with AI-mediated support seeking, this association was largely accounted for by distress levels, suggesting that anxiously attached individuals may turn to AI primarily due to heightened emotional need. Conversely, attachment avoidance emerged as a robust negative predictor after adjusting for distress, indicating a reluctance to engage with AI support even under conditions of need. Overall, findings position general-purpose GenAI as an accessible, low-barrier resource that is disproportionately utilized by distressed individuals, underscoring the need for research on the safety and efficacy of this emerging digital behavior.

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