Attachment Styles and AI Chatbot Interactions Among College Students

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 boundaries; and (3) the paradox of AI intimacy, capturing the tension between students' willingness to disclose personal information to AI while simultaneously recognizing its limitations as a relational partner. These findings suggest that attachment orientations play an important role in shaping how students experience and interpret their interactions with AI chatbots, extending attachment theory to the domain of human-AI interaction.

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