Generative AI is rapidly becoming embedded in design tools and workflows, accompanied by promises of increased productivity, reduced cognitive burden, and improved decision-making. These promises shape how AI systems are adopted and evaluated in design practice. However, research on design, automation, and sociotechnical work suggests that labor-saving technologies often reorganize work in unintended ways rather than simply reducing effort. In this paper, I examine AI-assisted design by focusing on how AI reshapes the cognitive, coordinative, and organizational conditions under which design work unfolds. The analysis is organized around myths and ironies of AI-assisted design, treating myths as early abstractions of design work that become institutionalized as technologies are embedded in practice. It identifies six widely articulated myths—including higher-level thinking, explainability, reduced cognitive load, simplified workflows, democratization, and objectivity—and the patterned ironies that follow as omitted cognitive and coordinative work reasserts itself in practice. The paper concludes with implications for AI-assisted design tools, professional practice, and design education.

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.

Professional roles for data visualization designers are growing in popularity, and interest in relationships between the academic research and professional practice communities is gaining traction. However, despite the potential for knowledge sharing between these communities, we have little understanding of the ways in which practitioners design in real-world, professional settings. Inquiry in numerous design disciplines indicates that practitioners approach complex situations in ways that are fundamentally different from those of researchers. In this work, I take a practice-led approach to understanding visualization design practice on its own terms. Twenty data visualization practitioners were interviewed and asked about their design process, including the steps they take, how they make decisions, and the methods they use. Findings suggest that practitioners do not follow highly systematic processes, but instead rely on situated forms of knowing and acting in which they draw from precedent and use methods and principles that are determined appropriate in the moment. These findings have implications for how visualization researchers understand and engage with practitioners, and how educators approach the training of future data visualization designers.

Design culture is increasingly present within organizations, especially with the rise of UX as a profession. Yet there are often disconnects between the development of a design philosophy and its translation in practice. Students preparing for UX careers are positioned in a liminal space between their educational experience and future practice, and are actively working to build a bridge between their developing philosophy of design and the translation of that philosophy when faced with the complexity of design practice. In this study, we interviewed ten students and practitioners educated within design-oriented HCI programs, focusing on their design philosophy and evaluating how their philosophical beliefs were shaped in practice. Building on prior work on flows of competence, we thematically analyzed these interviews, identifying the philosophical beliefs of these designers and their trajectories of development, adoption, or suppression in industry. We identify opportunities for enhancements to UX educational practices and future research on design complexity in industry contexts.

The rapid growth of the UX profession has led to an increased need for qualified practitioners and a proliferation of UX educational programs offered in both academia and industry. In this note, we present the design and initial evaluation of a new studio-based undergraduate program in UX--the first of its kind at a large, research-intensive US university. The program includes several curricular innovations, such as an integrated studio pedagogy in which six topical strands are interwoven across two types of studios. These studios are interconnected and span five semesters of the undergraduate experience. We present the curriculum model and the foundational principles that informed its design. We describe the two types of studios and their interconnection, and present early evaluation data showing that students are building valuable skills. The program described in this note provides a trailblazing model for UX pedagogy at the undergraduate level.

Complex cognitive activities, such as analytical reasoning, problem solving, and sense making, are often performed through the mediation of interactive computational tools. Examples include visual analytics, decision support, and educational tools. Through interaction with visual representations of information at the visual interface of these tools, a joint, coordinated cognitive system is formed. This partnership results in a number of relational properties—those depending on both humans and tools—that researchers and designers must be aware of if such tools are to effectively support the performance of complex cognitive activities. This article presents 10 properties of interactive visual representations that are essential and relational and whose values can be adjusted through interaction. By adjusting the values of these properties, better coordination between humans and tools can be effected, leading to higher quality performance of complex cognitive activities. This article examines how the values of these properties affect cognitive processing and visual reasoning and demonstrates the necessity of making their values adjustable—all of which is situated within a broader theoretical framework concerned with human‐information interaction in complex cognitive activities. This framework can facilitate systematic research, design, and evaluation in numerous fields including information visualization, health informatics, visual analytics, and educational technology.