Data visualization practitioners routinely invoke inspiration, yet we know little about how it is constructed in public conversations. We conduct a discourse analysis of 31 episodes from five popular data visualization podcasts. Podcasts are public-facing and inherently performative: guests manage impressions, articulate values, and model “good practice” for broad audiences. We use this performative setting to examine how legitimacy, identity, and practice are negotiated in community talk. We show that “inspiration talk” is operative rather than ornamental: speakers legitimize what counts, who counts, and how work proceeds. Our analysis surfaces four adjustable evaluation criteria by which inspiration is judged—novelty, authority, authenticity, and affect—and three operative metaphors that license different practices—spark, muscle, and resource bank. We argue that treating inspiration as a boundary object helps explain why these frames coexist across contexts. Findings provide a vocabulary for examining how inspiration is mobilized in visualization practice, with implications for evaluation, pedagogy, and the design of galleries and repositories that surface inspirational examples.

Visualization design is often described as a process of solving a well-defined problem by navigating a design space. While existing visualization design models have provided valuable structure and guidance, they tend to foreground technical problem-solving and underemphasize the interpretive, judgment-based aspects of design. In contrast, research in other design disciplines has emphasized the importance of framing-how designers define and redefine what the problem is-and the co-evolution of problem and solution spaces through reflective practice. These dimensions remain underexplored in visualization research, particularly from the perspective of expert practitioners. This paper investigates how visualization designers frame problems and navigate the interplay between problem understanding and solution development. We conducted a mixed-methods study with 11 expert design practitioners using design challenges, diary entries, and semi-structured interviews. Through reflexive thematic analysis, we identified key strategies that participants used to frame design problems, reframe them in response to evolving constraints or insights, and construct bridges between problem and solution spaces. These included the use of metaphors, heuristics, sketching, primary generators, and reflective evaluation of failed or incomplete ideas. Our findings contribute an empirically grounded account of visualization design as a reflective, co-evolutionary practice. We show that framing is not a preliminary step, but a continuous activity embedded in the act of designing. Participants frequently shifted their understanding of the problem based on solution attempts, feedback from tools, and ethical or narrative concerns. These insights extend current visualization design models and highlight the need for frameworks that better account for framing and interpretive judgment. We conclude with implications for visualization research, education, and practice. In particular, we discuss how design education can better support framing and co-evolutionary thinking, and how visualization research can benefit from greater attention to the cognitive strategies and reflective processes that underpin expert design.

As generative AI tools become integrated into design workflows, students increasingly engage with these tools not just as aids, but as collaborators.This study analyzes reflections from 33 student teams in an HCI design course to examine the kinds of judgments students make when using AI tools.We found both established forms of design judgment (e.g., instrumental, appreciative, quality) and emergent types: agency-distribution judgment and reliability judgment.These new forms capture how students negotiate creative responsibility with AI and assess the trustworthiness of its outputs.Our findings suggest that generative AI introduces new layers of complexity into design reasoning, prompting students to reflect not only on what AI produces, but also on how and when to rely on it.By foregrounding these judgments, we offer a conceptual lens for understanding how students engage in co-creative sensemaking with AI in design contexts.

The complexity of UX design practice extends beyond ill-structured design problems to include uncertainties shaped by shifting stakeholder priorities, team dynamics, limited resources, and implementation constraints.While prior research in related fields has addressed uncertainty in design more broadly, the specific character of uncertainty in UX practice remains underexplored.This study examines how UX practitioners experience and respond to uncertainty in real-world projects, drawing on a multi-week diary study and follow-up interviews with ten designers.We identify a range of practitioner strategies-including adaptive framing, negotiation, and judgment-that allow designers to move forward amid ambiguity.Our findings highlight the central role of design judgment in navigating uncertainty, including emergent forms such as temporal and sacrificial judgment, and extend prior understandings by showing how UX practitioners engage uncertainty as a persistent, situated feature of practice.

Inspiration plays an important role in design, yet its specific impact on data visualization design practice remains underexplored. This study investigates how professional visualization designers perceive and use inspiration in their practice. Through semi-structured interviews, we examine their sources of inspiration, the value they place on them, and how they navigate the balance between inspiration and imitation. Our findings reveal that designers draw from a diverse array of sources, including existing visualizations, real-world phenomena, and personal experiences. Participants describe a mix of active and passive inspiration practices, often iterating on sources to create original designs. This research offers insights into the role of inspiration in visualization practice, the need to expand visualization design theory, and the implications for the development of visualization tools that support inspiration and for training future visualization designers.