arXiv subjects

Ali Baigelenov

Publications and source records attributed to Ali Baigelenov.

7 recordsLinked to original sources

arXiv2026

Design Knowledge in Data Visualization: Mapping the Epistemic Landscape

Data visualization research has developed many influential forms of design knowledge, including perceptual principles, design guidelines, process models, and formalized representations of design constraints. These contributions have been effective at articulating explicit, portable, and codified forms of knowledge. Yet the broader landscape on which visualization design depends remains less clearly articulated, especially with respect to intermediate-level knowledge, precedents, tacit repertoires, and situated forms of knowing. In this paper, we draw on design theory to map this broader landscape of design knowledge in data visualization. Through this lens, we show how visualization research has built substantial strengths in some regions while leaving others comparatively underarticulated. We further argue that visualization design depends not only on knowledge artifacts such as theories, guidelines, and patterns, but also on knowledge-in-use---the situated interpretation, adaptation, and coordination of multiple forms of knowing in concrete design situations. This broader account has implications for how the field conceptualizes design expertise, evaluates and develops scholarly contributions, and approaches AI-assisted design. Rather than treating visualization design as either fully formalizable or wholly resistant to computational support, we argue for a differentiated view in which computational systems can support some forms of design knowing, while others remain inseparable from human judgment, contextual interpretation, and the ongoing reorganization of design work in practice.

cs.HC↗

arXiv2026

Situatedness in Visualization Design: Making Unresolved Work Actionable

Visualization design often proceeds under unresolved conditions---goals shift, data remain provisional, stakeholder needs evolve, and several plausible directions may remain available at once. Existing visualization frameworks help organize design work and articulate major decisions, yet offer limited explanation of how practitioners proceed before a path forward has become clear. Drawing on an episode-level analysis of a previously collected three-phase qualitative corpus involving eleven expert visualization practitioners, we examine situations in which the problem, representational target, or viable direction remained unsettled. We find that practitioners make such situations actionable through provisional local moves. These moves reveal patterns, distinctions, and interpretive possibilities; clarify what is tractable, viable, or worth pursuing; and sometimes reorient the work itself. The analysis shows that situated action, professional judgment, and explicit design reasoning are intertwined in expert practice. It also identifies a practical limit on how fully design activity can be specified in advance. When the meaning of the next move depends on what a situation reveals in response to action, prescriptive decision structures cannot fully determine the course of design. The paper contributes an empirical account of situatedness in visualization practice and explains how local action makes unresolved work interpretable enough for consequential design decisions.

cs.HC↗

arXiv2026

Talking Inspiration: A Discourse Analysis of Data Visualization Podcasts

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.

cs.HC↗

arXiv2025

Are Cognitive Biases as Important as they Seem for Data Visualization?

Research on cognitive biases and heuristics has become increasingly popular in the visualization literature in recent years. Researchers have studied the effects of biases on visualization interpretation and subsequent decision-making. While this work is important, we contend that the view on biases has presented human cognitive abilities in an unbalanced manner, placing too much emphasis on the flaws and limitations of human decision-making, and potentially suggesting that it should not be trusted. Several decision researchers have argued that the flip side of biases -- i.e., mental shortcuts or heuristics -- demonstrate human ingenuity and serve as core markers of adaptive expertise. In this paper, we review the perspectives and sentiments of the visualization community on biases and describe literature arguing for more balanced views of biases and heuristics. We hope this paper will encourage visualization researchers to consider a fuller picture of human cognitive limitations and strategies for making decisions in complex environments.

cs.HC↗

arXiv2025

De-skilling, Cognitive Offloading, and Misplaced Responsibilities: Potential Ironies of AI-Assisted Design

The rapid adoption of generative AI (GenAI) in design has sparked discussions about its benefits and unintended consequences. While AI is often framed as a tool for enhancing productivity by automating routine tasks, historical research on automation warns of paradoxical effects, such as de-skilling and misplaced responsibilities. To assess UX practitioners' perceptions of AI, we analyzed over 120 articles and discussions from UX-focused subreddits. Our findings indicate that while practitioners express optimism about AI reducing repetitive work and augmenting creativity, they also highlight concerns about over-reliance, cognitive offloading, and the erosion of critical design skills. Drawing from human-automation interaction literature, we discuss how these perspectives align with well-documented automation ironies and function allocation challenges. We argue that UX professionals should critically evaluate AI's role beyond immediate productivity gains and consider its long-term implications for creative autonomy and expertise. This study contributes empirical insights into practitioners' perspectives and links them to broader debates on automation in design.

cs.HC↗

arXiv2025

How Visualization Designers Perceive and Use Inspiration

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.

cs.HC↗

arXiv2020

Design Judgment in Data Visualization Practice

Data visualization is becoming an increasingly popular field of design practice. Although many studies have highlighted the knowledge required for effective data visualization design, their focus has largely been on formal knowledge and logical decision-making processes that can be abstracted and codified. Less attention has been paid to the more situated and personal ways of knowing that are prevalent in all design activity. In this study, we conducted semi-structured interviews with data visualization practitioners during which they were asked to describe the practical and situated aspects of their design processes. Using a philosophical framework of design judgment from Nelson and Stolterman [23], we analyzed the transcripts to describe the volume and complex layering of design judgments that are used by data visualization practitioners as they describe and interrogate their work. We identify aspects of data visualization practice that require further investigation beyond notions of rational, model- or principle-directed decision-making processes.

cs.HC↗