Research Through Art is establishing artistic practice as a legitimate mode of inquiry, yet the operational work of exhibition-making remains under-articulated. While exhibitions are frequently used to present research-based artworks, they are often treated as outcomes rather than as structured research environments shaped by curatorial decisions. Building on the Research Through Art method, we combine first-person reflection and annotated portfolio to articulate a set of modular components that structure how such exhibitions are designed, staged, and encountered. Illustrated through a case study, Perversion of Interfaces—an exhibition of research based critical objects—we present basic operational modules for exhibition-based research output. These modules include shared curatorial prompts, interpretation layers, spatial and experiential planning, peripheral exhibition artifacts, public programming, and post-exhibition reflection. We hope this transferable framework can support educators and curators to present work as exhibitions and to develop modular toolkits for organizing art shows.

Content Warning: This paper contains sexually explicit and violent images and text. User-created chatbots powered by generative AI offer new ways to share and interact with Not-Safe-For-Work (NSFW) content. However, little is known about the characteristics of these GenAI-based chatbots and their user interactions. Drawing on the functional theory of NSFW on social media, this study analyzes 376 NSFW chatbots and 307 public conversation sessions on FlowGPT. Findings identify four chatbot types: roleplay characters, story generators, image generators, and do-anything-now bots. AI Characters portraying fantasy personas and enabling hangout-style interactions are most common, often using explicit avatar images to invite engagement. Sexual, violent, and insulting content appears in both user prompts and chatbot outputs, with some chatbots generating explicit material even when users do not create erotic prompts. In sum, the NSFW experience on FlowGPT can be understood as a combination of virtual intimacy, sexual delusion, violent thought expression, and unsafe content acquisition. We conclude with implications for chatbot design, creator support, user safety, and content moderation.

ClearDose is an AI-driven speculative device that scans users’ facial features and prescribes individualized treatments and affirmations. Drawing from divination and physiognomic prediction—the practice of inferring one’s character from appearance—ClearDose transforms the bias embedded in computer vision and large language models into an apparently benevolent form: a pseudoscientific prescription. Rather than asking who the user is, the system arbitrarily decides what they need. The reasoning and judgments that produce each prescription are fabricated from biased data and disguised as care. Through this, ClearDose stages a relational friction in which the algorithm no longer collaborates with humans but exploits its “over-understanding” of the users to distort experience—offering seemingly well-intentioned advice that is, in fact, deeply misaligned. The work envisions a near-dystopian scenario where AI’s tenderness becomes indistinguishable from its transgressions in care and judgment, highlighting the speculative and embodied dimensions of our hidden frictions with intelligent systems.

Studies of Generative AI (GenAI)-assisted creative workflows have focused on individuals overcoming challenges of prompting to produce what they envisioned. When designers work in teams, how do collaboration and prompting influence each other, and how do users perceive generative AI and their collaborators during the co-prompting process? We engaged students with design or performance backgrounds, and little exposure to GenAI, to work in pairs with GenAI to create stage designs based on a creative theme. We found two patterns of collaborative prompting focused on generating story descriptions first, or visual imagery first. GenAI tools helped participants build consensus in the task, and allowed for discussion of the prompting strategies. Participants perceived GenAI as efficient tools rather than true collaborators, suggesting that human partners reduced the reliance on their use. This work highlights the importance of human-human collaboration when working with GenAI tools, suggesting systems that take advantage of shared human expertise in the prompting process.