Examination of why and how users customize social AI through the lens of the newly developed concept of AI individualism shows that, for many users, customization is a co-creative process between the human and the AI that is perceived as strengthening support, autonomy, ownership, and engagement, potentially contributing to a closer and more personal relationship.
Abstract
Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character.ai, we know little about how users actively shape social AI to reflect their personal preferences. This study examines why and how users (N = 169) customize social AI through the lens of the newly developed concept of AI individualism. Through reflexive thematic analysis of open-ended responses, we identified several motivations for customization, including (1) enhanced pragmatic support, (2) emotional support or companionship, (3) trust and reliability, (4) pushback, (5) a tailored degree of human likeness, (6) creativity or playfulness, and (7) having the AI function as an extension of the self. In line with the concept of AI individualism, our findings show that, for many users, customization is a co-creative process between the human and the AI that is perceived as strengthening support, autonomy, ownership, and engagement, potentially contributing to a closer and more personal relationship. Through customization users may come to view social AI as a personalized social resource that increases their sense of individualism, freedom, and control. We discuss how these perceptions may foster pseudo-autonomy, whereby customization creates an illusion of individual control over powerful social AI systems.
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
In both cultural groups, higher perceived well-being was consistently associated with greater acceptance of AI, while ethical concerns were associated with increased fear of AI.
Mohammad Mominur Rahman, A. Yankouskaya, S. Alshakhsi et al.· SN Computer Science· 0 citations
Introduction This study examines how perceived anthropomorphism of artificial intelligence (AI) is associated with users’ belongingness need through liking of AI and perceived emotional support. Drawing on the three-factor theory of anthropomorphism, the Computers as Social Actors paradigm, social support theory, and the social surrogate hypothesis, we propose that users’ human-like perceptions of AI are linked to belongingness-related motivation through a sequential socioemotional process. Methods Survey data were collected from 473 college students with prior experience using AI tools or conversational AI assistants. Participants completed measures of perceived anthropomorphism of AI, liking of AI, perceived emotional support, and belongingness need. Results Confirmatory factor analysis showed that the hypothesized four-factor model fit the data better than alternative models, providing preliminary evidence for the empirical distinctiveness of the focal constructs. Liking of AI and perceived emotional support each served as significant mediators in this association. In addition, the sequential indirect path from perceived anthropomorphism to liking of AI, perceived emotional support, and belongingness need was significant. Discussion These findings suggest that anthropomorphic perceptions of AI are related to users’ belongingness-related motivation through layered affective and socioemotional processes. The study contributes to human–AI interaction research by distinguishing liking of AI from perceived emotional support and by clarifying how anthropomorphic AI perceptions are associated with users’ social–emotional experiences. The findings should be interpreted within the limits of the cross-sectional design and the outcome measure: belongingness need reflects users’ desire for acceptance and connection rather than actual belongingness satisfaction.
Jinrui Tian, Boxuan Li, Ronghua Zhang et al.· Frontiers in Psychology· 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 use of artificial intelligence in our lives has become incredibly common. We engage with AI-based content daily, whether it is through our social networks, schools, entertainment, jobs, or elsewhere. As technology advances, we should be wondering if humans will continue to place a premium on experiences based primarily on their own work and feelings. This research examines how people define "authenticity" in relation to their experience, as well as whether or not they will pay a premium for authentic experiences. Data collection consisted of an electronic questionnaire that was analyzed using various statistical analyses; the Human Authenticity Index (HAI) was also created to determine how participants regarded authenticity. Findings revealed that trust, transparency, emotional connections and visible evidence of human effort were important factors to consumers. A significant amount of participants stated they would prefer human produced experiences over AI-created experiences, especially in instances involving empathy, creativity, and interaction. These findings suggest that as automated content becomes more prevalent, authentic experiences will become progressively more valuable. The data provides insight that authenticity may become a substantive source of value in future markets.
Sugunapriya T, S. K· EPRA international journal o...· 0 citations
Large language models (LLMs) are popular tools for creative ideation, but have been shown to homogenize outputs across users. We test if approaching an AI tool with high human (vs. low) human agency can mitigate this homogenization effect by encouraging people to use AI to augment their creativity, rather than offload it. Participants were experimentally assigned to one of three conditions (high-agency approach with AI access, low-agency approach with AI access, or a human-only control) and generated creative uses for everyday objects. Contrary to our expectations, ideas did not differ in individual-level quality (overall creativity, originality, and usefulness). However, a preregistered similarity-to-centroid analysis and an exploratory cluster analysis provided convergent evidence of AI-induced homogenization among the low-agency condition. Thus, while AI has enabled greater ideational fluency, our research suggests the degree of agency with which people approach their AI tool has downstream consequences on collective creativity.
Sarah H. Wu, Yuewen Yang, A. Y. Lee et al.· Creativity & Cognition· 0 citations
