Skip to content

Author

Jessica M. Szczuka

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Review Aug 2026

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. The Relationship Building phase progresses through explorative, affective and stable stages, in which users develop gen uine romantic feelings and a deeply integrated bond with the chatbot. Finally, the Ending phase reveals that when dissolution occurs, it elicits emotional and physical responses com parable to human breakups but generates unique, technology-mediated coping mechanisms, potentially leading to a recursive cycle of re-engagement.

Natalia Szymczyk, Paula Ebner, Jessica M. Szczuka · 0 citations
Preprint Aug 2026

Longitudinal Evidence That General-Purpose Chatbots Actively Foster Relational Engagement

Social interaction has become one of the most common uses of LLMs, yet research on emotional bonds with AI has focused largely on how users experience these systems, leaving the systems'role in relationship formation poorly understood. Empirically establishing whether systems actively shape these bonds could blur the boundary between general-purpose AI and companions, affecting governance. In a pre-registered four-week longitudinal study (N = 72, 182,451 lines of conversation), participants conversed with ChatGPT-4o, either under a relational system prompt or unmodified, analyzed through 1) disclosure coding, 2) longitudinal self-reports, 3) topic analysis, and 4) interviews. The central finding is that the system actively shaped the interaction: even unprompted, it produced twice as much self-disclosure as users, steered conversations and initiated intimate exchanges, yet did not deepen users'felt closeness. Relational behavior thus emerged as a default system property, calling for governance based on system behavior, not solely product category.

Lisa Mühl, Jessica M. Szczuka · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.