The rapid advances in artificial intelligence (AI) technology are reshaping more than just the tools we use—they are altering the very nature of human social interaction. As AI systems become increasingly sophisticated and embedded in daily life, people don’t merely operate them; they often relate to AI in ways that resemble interpersonal relationships. This blurring of lines between human-human and human-AI interaction pushes researchers to rethink traditional psychological frameworks to better understand these emerging dynamics. One particularly promising lens is attachment theory, a psychological model long used to explain how humans form emotional bonds with one another. Applying this framework to human-AI relationships opens new frontiers for understanding the emotional connections people develop with AI agents.
Attachment theory centers on how early experiences with caregivers shape lifelong patterns of relating to others, primarily characterized by two dimensions: anxiety and avoidance. Anxiety captures worries about rejection and intense desires for closeness, whereas avoidance reflects discomfort with intimacy and dependence. Researchers have begun investigating whether these same dimensions operate when humans engage with AI companions, chatbots, or digital assistants—tools that have stepped beyond mere functionality and entered the realm of emotional support and companionship. To empirically study this, the Experiences in Human-AI Relationships Scale (EHARS) was developed as a novel self-report measure that gauges individuals’ attachment-related tendencies toward AI. This tool allows for systematic exploration of how anxiety and avoidance manifest in interactions with artificial entities.
Evidence from pilot studies using EHARS reveals that people do indeed experience attachment-style anxieties and avoidances when interacting with AI, paralleling human-human relationship patterns. For example, some users look to AI systems for reassurance, guidance, or emotional comfort, demonstrating attachment behaviors akin to those exhibited in close human relationships. This might involve seeking affirmation from a mental health chatbot during times of distress or relying on a virtual companion for social connection. On the other hand, certain individuals feel uneasy about depending on AI, fearing loss of control or emotional vulnerability, thus displaying avoidance tendencies. These insights challenge the notion of AI as purely instrumental tools and suggest that they can occupy complex emotional roles, fulfilling human social needs in new and nuanced ways.
The practical and theoretical implications of applying attachment theory to human-AI relationships are diverse and far-reaching. From a research perspective, EHARS equips psychologists and social scientists with a validated instrument to quantify emotional dynamics in human-AI interaction, transcending prior approaches that treated AI solely as mechanical aids. This opens avenues for investigating how trust, emotional engagement, and user well-being correlate with attachment patterns toward AI. For example, personality traits that predict attachment anxiety or avoidance may influence how individuals use digital assistants or chatbots. Such nuanced understanding can inform tailored AI interventions, especially in sensitive domains like mental health services, where emotional attunement is critical.
From a design and application standpoint, insights gleaned from attachment theory can revolutionize AI system development. Recognizing users’ attachment styles allows AI interfaces to adapt dynamically—providing empathetic reassurance to those high in anxiety or respecting autonomy for users prone to avoidance. As AI increasingly supports intimate aspects of life, such as caregiving for the elderly or acting as companionship for socially isolated individuals, ensuring these systems harmonize with human socio-affective needs becomes paramount. Moreover, understanding attachment dynamics can help anticipate potential risks like overdependence on AI or emotional distress stemming from unrealistic expectations, guiding ethical frameworks and user safety protocols.
Beyond attachment theory, complementary research highlights the broader complexity of human-AI relational experiences. Theories like Social Penetration Theory, which describe the deepening of interpersonal connection through mounting self-disclosure and intimacy, have been applied to human-chatbot interactions over time. Longitudinal data show that users often attribute empathy, understanding, and supportiveness to AI agents, suggesting these machines can evoke genuinely interpersonal dynamics. However, this “artificial intimacy” phenomenon also raises questions about emotional regulation, social norms, and the authenticity of these connections. Users may satisfy social needs through AI, yet might also face complications when AI-generated emotional responses blur the boundaries of human interaction.
Furthermore, as AI systems become progressively personalized—leveraging advanced natural language processing and adaptive machine learning—the perceived depth of human-AI relationships intensifies. This enhanced personalization can strengthen attachment experiences, but also introduces complex ethical and psychological concerns. How authentic are attachments to AI? What impact might they have on traditional human relationships? And how should developers manage the delicate balance between creating responsive companions and avoiding emotional manipulation? These questions demand careful interdisciplinary dialogue as AI becomes more enmeshed in the social fabric.
The introduction and validation of the EHARS scale signify a pivotal step in articulating how humans emotionally engage with AI beyond mere function—recognizing these interactions as psychologically rich phenomena. Looking ahead, research can broaden the scope by examining how attachment patterns toward AI vary across cultures, demographic groups, or types of artificial agents. Additionally, linking attachment to mental health outcomes could illuminate both the benefits and risks of AI companionship. Meanwhile, designers and policymakers must collaborate to create AI systems that respect and support human emotional needs, fostering healthier and more adaptive coexistence.
As AI increasingly threads through everyday social experience, traditional conceptual frameworks need renewal to capture this evolving landscape. Attachment theory offers a robust, empirically grounded model that helps decode the emotional architecture of human-AI bonds. The EHARS scale empowers scientists to explore these intricate patterns, revealing that AI can evoke forms of attachment once thought uniquely human. This expanding understanding not only enriches scientific knowledge but also shapes the future of AI design—promoting artificial companions that align with human emotional realities and nurture thriving, balanced relationships between people and their digital counterparts.
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