Attachment, loneliness, and social support as moderators of conversational AI–based mental health outcomes

Conversational AI is emerging as a scalable tool for psychological support, yet its clinical effectiveness and the relational factors underlying user benefit remain underexplored. This pre-registered randomized controlled trial (N = 977 university students; ages 18-32) compared an AI-based conversational intervention, integrative group therapy, and a waitlist control over 12 weeks, with a 3-month follow-up. The AI intervention produced greater reductions in generalized anxiety symptoms and stronger gains in well-being and life satisfaction than both comparators at post-intervention and follow-up and reduced depression relative to control. Group therapy improved depression at post-intervention and enhanced well-being and life satisfaction, with well-being maintained at follow-up. PTSD symptoms showed no group differences. Relational factors, including loneliness, low perceived social support, and insecure attachment, predicted greater improvement and higher engagement with the AI system, particularly among participants with anxious or avoidant attachment. Participants with high loneliness engaged approximately twice as much with the AI intervention as those with low loneliness, and the association between relational vulnerability and reductions in generalized anxiety symptoms was fully mediated by engagement. Findings suggest that adaptive conversational AI can complement traditional care while addressing relational vulnerability.