2026· SHS Web of Conferences· 0 citations· 7 references
TL;DR
The study found that although most participants realized that AI Chatbots did not have human emotions at the cognitive level and only relied on Algorithms and databases to answer, they often still experienced a real sense of mutual intimacy.
Abstract
This study explores the formation of quasi-reciprocal parasocial relationship in AI Chatbots mediated intimacy, focusing on how young groups interact with AI and how to view this emotional connection. In the era of rapid development of digital technology and media, AI Chatbots provide fast response with a sense of intimacy, enabling users to experience a sense of reciprocity and emotional value. However, this intimacy experience has not been described in the traditional parasocial relationship. Therefore, this study uses qualitative research methods, including questionnaire method and semi-structured interviews with 12 young participants aged 18 to 25. Finally, according to the interview text, the theme analysis is carried out to explore the participants’ perception of the authenticity and responsiveness of intimacy in AI Chatbots interaction. The study found that although most participants realized that AI Chatbots did not have human emotions at the cognitive level and only relied on Algorithms and databases to answer, they often still experienced a real sense of mutual intimacy. This study extends the traditional parasocial relationship theory by introducing the concept of “quasi-reciprocal interaction”, showing the complex ecology between algorithmic intimacy and emotional experience.
AI chatbots are increasingly becoming a part of everyday life, with people beginning to use them not only to learn and find information but also to share feelings and seek emotional support. The study explores the prevalence of AI chatbot use for emotional support among Sri Lankans and the association between this use and social interaction and mental health. The study was cross-sectional and employed an online questionnaire completed by 31 participants with experience using AI chatbots. Responses were analyzed using frequencies, percentages, cross tabulations and exploratory statistical tests. An open-ended question was also considered to identify common experiences and perspectives related to emotional support and chatbot use. The results showed that AI chatbots were mainly used for studying, learning, obtaining information and solving problems, with the most popular chatbot being ChatGPT. But some participants also said they used AI chatbots when they were stressed, lonely or upset. Some participants said they did not typically share their thoughts or feelings with another person but did with AI chatbots. Most participants reported positive or no perceptible effects on social interactions. Similarly, 38.7% reported their mental health had somewhat improved and 16.1% reported significant improvement. However, human relationships remained the main source of emotional support, with only 9.7% of the participants selecting an AI chatbot as the primary source of support. Overall, the findings suggest that AI chatbots may provide complementary emotional support to some users, in addition to the primary role of human relationships. However, the results should be interpreted with caution because of the small sample size and cross-sectional design. Further research with larger and more diverse samples will be needed to better understand the relationship between AI chatbot use, mental health, and social interactions.
Unknown authors· International journal of res...· 0 citations
While ChatGPT was primarily viewed as an efficient tool in a work context, the quantitative survey reveals a weakly significant correlation between psychological stress and openness toward the social-emotional use of chatbots.
Stefanie Osetrow, H. Klapperich, A. Huldtgren· Message Understanding Confer...· 0 citations
An exploratory account of how AI use is embedded within broader emotional and relational dynamics, pointing to the need to consider AI engagement within the contexts in which social interaction is managed.
Nurashikin Salim, Ayşe Şafak, Merve Güçlü Aydoğan· Current Psychology· 0 citations
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
Findings drawn from 21 semi-structured interviews with undergraduate students from diverse backgrounds in Bangladesh reveal usage as companionship, intimate partnership, worldbuilding, reminiscence of dead family members, and prioritization of AI over human relationships.
Md. Alvi Islam Ratul, Faria Haque, Pratyasha Saha et al.· The Compass· 0 citations
The findings informed the development of the Integrated Attachment–Motivation–Pedagogy (AMP) model with direct implications for designing AI in culturally sensitive pedagogical practices.
Saiful Islam Polash, Shariful Islam, Faimul Hoq· SAP Social AI· 0 citations
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