Attachment Styles Towards Artificial Intelligence: Exploring Emotional Bond Formation Among College Students

Artificial intelligence (AI) has increasingly transitioned from a functional technological tool to a psychologically meaningful presence in the daily lives of college students. Beyond academic assistance, AI systems are now frequently used for emotional expression, companionship, and non-judgmental support, raising important questions regarding emotional bonding and attachment-like relationships with non-human agents. Drawing on attachment theory, the present study aims to examine how attachment styles—specifically attachment anxiety and attachment avoidance—are associated with emotional bonding, trust, and reliance on AI among college students. The study adopts a quantitative research design with a sample of 60 undergraduate students aged 18–23 years from a university in Mohali, India. Data will be collected using an adapted version of the Experiences in Close Relationships (ECR) Scale to assess attachment anxiety and avoidance toward AI, along with selected subscales of the Godspeed Questionnaire Series measuring anthropomorphism, likeability, and trust. Responses will be obtained through online questionnaires, and descriptive and correlational analyses will be conducted to explore relationships between attachment dimensions and emotional engagement with AI. It is anticipated that students with higher attachment anxiety will report stronger emotional bonds, greater trust, and increased reliance on AI for emotional support, whereas attachment avoidance may be associated with more instrumental and emotionally distant patterns of use. The expected findings aim to position AI not merely as a technological tool but as an emerging relational entity in students’ psychological lives. This study contributes to the growing literature on human–AI interaction by applying attachment theory to understand evolving emotional dynamics, while also highlighting implications for mental health practice, ethical AI design, and the prevention of over-reliance on artificial agents.