Intimate Strangers by Design: A Uses and Gratifications Analysis of AI Companionship
- URL: http://arxiv.org/abs/2604.06419v1
- Date: Tue, 07 Apr 2026 19:53:58 GMT
- Title: Intimate Strangers by Design: A Uses and Gratifications Analysis of AI Companionship
- Abstract summary: Conversational AI companions have grown prominent in public discourse, yet scholarly understanding of user experiences remains limited.<n>This study offers three contributions: first, participants reported gratifications mapping onto established Uses and Gratifications (U&G) categories but qualitatively inflected by conversational AI's distinctive affordances.<n>Second, several gratifications, creative collaboration as relational co-production, relational simulation as interpersonal training, do not map onto existing typologies, instead emerging through interactive processes in which users actively simulate experiences with AI.<n>Third, gratifications shifted over time, moving from instrumental entry points toward emotional engagement and
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Conversational AI companions have grown prominent in public discourse, yet scholarly understanding of user experiences remains limited, with existing research organized around evaluative poles of harm and benefit rather than examining what users seek, how affordances mediate need fulfillment, or how use evolves over time. Drawing on interviews with 20 users of AI companionship platforms and qualitative content analysis informed by Uses and Gratifications (U&G) theory, this study offers three contributions. First, participants reported gratifications mapping onto established U&G categories but qualitatively inflected by conversational AI's distinctive affordances, such as persistent availability, personalization, and absence of social judgment. Second, several gratifications, creative collaboration as relational co-production, relational simulation as interpersonal training, and sexual/romantic satisfaction as reclamation, do not map onto existing typologies, instead emerging through interactive processes in which users actively simulate experiences with AI. Third, gratifications shifted over time, moving from instrumental entry points toward emotional engagement and, in some cases, self-regulated moderation after therapeutic functions were fulfilled. These findings extend U&G by identifying gratification processes unique to interactive AI and suggest governance efforts would benefit from an empirically grounded understanding of how and why users engage with AI companions.
Related papers
- Socrates went Nuclear: Comparing Interaction Strategies for AI systems in a Learning Context using Brain Sensing [66.03354630509942]
This paper reports on a study where we compare three designs for user-AI interaction in a learning context.<n>Fifty study participants were tasked with learning about nuclear safety protocols.
arXiv Detail & Related papers (2026-09-01T02:24:35Z) - Informal Learning Emerges in Everyday Human-LLM Interaction [74.53081471113929]
We show that everyday human-LLM interaction contains measurable, selective and conditionally organized behavioural signatures of informal learning.<n>They shift AI evaluation from answer-delivery efficiency toward the preservation of cognitive opportunities for users to reason, test ideas and construct understanding.
arXiv Detail & Related papers (2026-07-20T07:56:41Z) - Proact-VL: A Proactive VideoLLM for Real-Time AI Companions [52.23988809605433]
We instantiate AI companions through two gaming scenarios, commentator and guide, selected for automatic evaluation.<n>We present Proact-VL, a framework that shapes multimodal language models into proactive, real-time interactive agents capable of human-like environment perception and interaction.
arXiv Detail & Related papers (2026-03-03T19:02:46Z) - Attachment Styles and AI Chatbot Interactions Among College Students [1.334956439319062]
This study explored how college students with different attachment styles describe their interactions with ChatGPT.<n>We identified three main themes: (1) AI as a low-risk emotional space, (2) attachment-congruent patterns of AI engagement, and (3) the paradox of AI intimacy.
arXiv Detail & Related papers (2025-12-20T18:49:07Z) - Taxonomy of User Needs and Actions [51.86289485979439]
The growing ubiquity of conversational AI highlights the need for frameworks that capture users' instrumental goals and the situated, adaptive, and social practices through which they achieve them.<n>To address this gap, we introduce the Taxonomy of User Needs and Actions (TUNA), an empirically grounded framework developed through iterative qualitative analysis of 1193 human-AI conversations.<n> TUNA organizes user actions into a three-level hierarchy encompassing behaviors associated with information seeking, synthesis, procedural guidance, content creation, social interaction, and meta-conversation.
arXiv Detail & Related papers (2025-10-07T17:04:42Z) - Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational Theory [18.716972390545703]
We examined how engaging with AICCs shaped wellbeing and how users perceived these experiences.<n>Findings revealed mixed effects -- greater affective and grief expression, readability, and interpersonal focus.<n>We offer design implications for AI companions that scaffold healthy boundaries, support mindful engagement, support disclosure without dependency, and surface relationship stages.
arXiv Detail & Related papers (2025-09-26T15:47:37Z) - "My Boyfriend is AI": A Computational Analysis of Human-AI Companionship in Reddit's AI Community [28.482163389070646]
We present the first large-scale computational analysis of r/MyBoyfriendIsAI, Reddit's primary AI companion community.<n>Our findings reveal how community members' AI companionship emerges unintentionally through functional use rather than deliberate seeking.
arXiv Detail & Related papers (2025-09-14T19:00:40Z) - Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike.
We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
arXiv Detail & Related papers (2024-10-28T23:10:06Z) - Interrogating AI: Characterizing Emergent Playful Interactions with ChatGPT [10.907980864371213]
This study focuses on playful interactions exhibited by users of a popular AI technology, ChatGPT.<n>We found that more than half (54%) of user discourse revolved around playful interactions.<n>It examines how these interactions can help users understand AI's agency, shape human-AI relationships, and provide insights for designing AI systems.
arXiv Detail & Related papers (2024-01-16T14:44:13Z) - Interactive Natural Language Processing [67.87925315773924]
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP.
This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
arXiv Detail & Related papers (2023-05-22T17:18:29Z) - Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A
Survey [71.43956423427397]
We aim to identify the nonverbal cues and computational methodologies resulting in effective performance.
This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings.
Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
arXiv Detail & Related papers (2022-07-20T13:37:57Z) - Identifying Ethical Issues in AI Partners in Human-AI Co-Creation [0.7614628596146599]
Human-AI co-creativity involves humans and AI collaborating on a shared creative product as partners.
In many existing co-creative systems, users communicate with the AI using buttons or sliders.
This paper explores the impact of AI-to-human communication on user perception and engagement in co-creative systems.
arXiv Detail & Related papers (2022-04-15T20:41:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.