Early-stage concept envisioning is a critical juncture in AI design, shaping how designers frame problems and the decisions that follow. Yet values and potential harms are often too abstract or addressed too late to meaningfully shape design. Using a Research-through-Design (RtD) approach, we developed the AI Concept Envisioning Toolkit, comprising an AI Capability Library, 24 Value–Harm Cards, and a Value–Tension Map, to support reasoning by juxtaposing values and harms within AI technical capabilities. Through a survey with 30 designers and in-depth interviews with 12 designers, we find that the toolkit is clear and perceived as valuable, and that it encourages value reflection, helps anticipate potential harms, and makes ethical considerations more transparent in early-stage design. We reflect on our design process and discuss design approaches for tools that promote reflection on values and potential harms, surface and navigate value tensions, and introduce productive friction throughout design workflows.

As AI integrates into design practice, designers increasingly use generative AI tools to envision AI-enabled solutions, positioning AI as both design tool and design material. This dual role creates recursive value tensions distinct from traditional design work. We engaged 18 designers in a concept envisioning activity and interviews to understand how they navigate values and recognize potential harms in this context. Our analysis reveals that (i) designers engage in reciprocal reflection-in-action with AI; (ii) this process surfaces multi-level value tensions across tool, designer, and concept; (iii) designers demonstrate greater attunement to harm recognition as a primary design signal than to articulating positive value fulfillment; and (iv) designers exercise anticipatory judgment through meta-design reasoning about how tool assumptions risk propagating into designed concepts and future use contexts. We extend Schön’s reflection-in-action framework and discuss implications for redesigning AI-mediated design tools, supporting harm-centered reasoning, and positioning design as foundational to AI development.

Meta’s brief release of its social AI chatbots highlighted the challenges of studying systems that are both short-lived and relationally complex. In response, we conducted a 10-day collaborative autoethnography to rapidly and meaningfully engage with the product. As the first application of this method to social AI chatbots, our study demonstrates its value for examining ephemeral and emotionally complex AI systems.

As user-generated video dominates media landscapes, it poses an accessibility challenge. While disability advocacy groups globally have secured hard-won accessibility regulations for broadcast media, no such regulation of user-generated content exists. Yet, one major player in this shift, TikTok, has a culture of user-generated, creative captioning. We sought to understand how TikTok videos are captioned and the impact current practices have on those who need captions to access audio content. Therefore, we conducted a content analysis of 300 open-captioned TikToks and contextualized these findings by interviewing nine caption users. We found that the current state of TikTok captioning does facilitate access to the platform but that a user-generated, social video-specific standard for captioning could improve caption quality and expand access. We contribute an empirical account of the state of TikTok captioning and outline steps toward a standard for user-generated captioning.