Attachment Styles and AI Chatbot Interactions Among College Students
Ziqi Lin, Taiyu Hou

TL;DR
This study explores how college students' attachment styles influence their interactions with AI chatbots, revealing diverse engagement patterns and perceptions of AI as a relational and emotional space.
Contribution
It extends attachment theory to human-AI interactions by qualitatively analyzing how attachment styles shape students' chatbot engagement and perceptions.
Findings
AI perceived as a low-risk emotional space
Secure attachment linked to supportive AI use
Avoidant attachment linked to boundary-maintaining AI use
Abstract
The use of large language model (LLM)-based AI chatbots among college students has increased rapidly, yet little is known about how individual psychological attributes shape students' interaction patterns with these technologies. This qualitative study explored how college students with different attachment styles describe their interactions with ChatGPT. Using semi-structured interviews with seven undergraduate students and grounded theory analysis, we identified three main themes: (1) AI as a low-risk emotional space, where participants across attachment styles valued the non-judgmental and low-stakes nature of AI interactions; (2) attachment-congruent patterns of AI engagement, where securely attached students integrated AI as a supplementary tool within their existing support systems, while avoidantly attached students used AI to buffer vulnerability and maintain interpersonal…
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Taxonomy
TopicsAI in Service Interactions · Digital Mental Health Interventions · Artificial Intelligence in Healthcare and Education
