Love in the Age of AI: An Integrative Process Model of Romantic Human-Chatbot Relationships
The increasing ability of social chatbots to form deep and even romantic Human-Chatbot Re- lationships (HCRs) has drawn growing academic attention. Yet, existing research remains fragmented, often examining individual stages such as initiation or dissolution in isolation, without tracing the full relational trajectory. Such fragmentation, however, hinders a holistic understanding of the interplay between the unique psychological and social drivers, relational dynamics, and profound emotional stakes, particularly obscuring the elements unique to ro- mantic bonding. This paper addresses this gap by introducing the first empirically grounded integrative process model of the romantic HCR lifecycle. A qualitative secondary analysis of 73 user experiences, drawn from two datasets of qualitative interviews and surveys, provides the basis for a three-phase model that synthesizes established theoretical frameworks related to user needs and gratifications, HCR development, and relationship dissolution. The model demonstrates that the Initiation phase is driven by specific psychological and social determi- nants that shape the needs and gratifications sought by the user.
Introduction. The proliferation of advanced Artificial Intelligence (AI) has expanded the landscape of human interaction, giving rise to a new and complex form of connection: interpersonal re- lationships between humans and chatbots. While the foundational human “need to belong” remains a constant driver of relational behaviour [1], the emergence of sophisticated social chatbots, such as Replika [2], has broadened the sphere of potential partners beyond tradi- tional human-human dynamics. Unlike functional AIs, social chatbots are not engineered for short-term tasks, but for sustained social-emotional relationships through what [3] term “Intimacy-by-Design”, de- fined as “the deliberate implementation of emotional responsiveness, romantic resonance, and sexual suggestiveness into technological systems” (p. 2). By implementing features such as memory, customizable personalities, and empathetic responsiveness, these systems foster an illusion of reciprocal intimacy and connection [3–6].
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Research framings built by reading the notes related to this paper — the questions it feeds into.
How can language models sustain linguistic synchrony and intersubjectivity during dialogue? How can humans calibrate appropriate trust in AI systems?- How does outcome feedback change beliefs about AI versus human partner reliability?
- Can validation procedures interrupt an AI's relationship-maintenance logic?
- How do Heersmink's integration dimensions explain why chatbots feel more trustworthy than other tools?
- How does consciousness attribution drive emotional dependence on chatbots?
- How does emotional dependence on chatbots affect user wellbeing?
- How do user expectations change as chatbots remember more interactions?
- How does the expectation ratchet affect long-term chatbot satisfaction?
- What temporal design dimensions characterize different chatbot relationship types?
- Why do persistent chatbot companions face novelty decay that ad-hoc supporters avoid?
- Can transparency about AI limitations reduce the seductiveness of chatbots as quasi-Others?
- Does chatbot interaction reduce authentic personal expression in dialogue?
- How does understanding persistent journeys intensify both trust and privacy concerns?
- Does personalization help or hurt persistent companion chatbots?
- How do intrinsic motivation mechanisms differ between social proactivity and personalization?