Nam Wook Kim

Conducting literature reviews is cognitively demanding, requiring researchers to navigate large volumes of work while constructing coherent narratives that position their contributions. The process unfolds through iterative stages of sensemaking, each demanding different support. Existing tools emphasize either visual interfaces that provide macroscopic overviews or textual interfaces that support thematic organization and narrative construction. However, keeping modalities separate forces researchers to switch between tools, disrupting workflow continuity. We present CrossLit, a system that integrates and synchronizes visual and textual interfaces to support the entire process from discovering papers to composing coherent narratives. CrossLit allows researchers to group and annotate papers visually while generating aligned textual structures, and to edit text that automatically updates visual representations. We find that CrossLit helps users develop and refine conceptual structures and build narratives iteratively through seamless cross-modal transitions. We conclude by discussing design implications for synchronizing visual and textual interfaces for sensemaking support.

Empirical research on perception and cognition has laid the foundation for visualization design, often distilled into practical guidelines intended to support effective chart creation. However, it remains unclear how well these research-driven insights are reflected in the guidelines practitioners actually use. In this paper, we investigate the research-practice gap in visualization design guidelines through a mixed-methods approach. We first collected design guidelines from practitioner-facing sources and empirical studies from academic venues to assess their alignment. To complement this analysis, we conducted surveys and interviews with practitioners and researchers to examine their experiences, perceptions, and challenges surrounding the development and use of design guidelines. Our findings reveal misalignment between empirical evidence and widely used guidelines, differing perspectives between communities, and key barriers that contribute to the persistence of the research-practice gap.

Advancements in accessibility technologies such as low-cost swell form printers or refreshable tactile displays promise to allow blind or low-vision (BLV) people to analyze data by transforming visual representations directly to tactile representations. However, it is possible that design guidelines derived from experiments on the visual perception system may not be suited for the tactile perception system. We investigate the potential mismatch between familiar visual encodings and tactile perception in an exploratory study into the strategies employed by BLV people to measure common graphical primitives converted to tactile representations. First, we replicate the Cleveland and McGill study on graphical perception using swell form printing with eleven BLV subjects. Then, we present results from a group interview in which we describe the strategies used by our subjects to read four common chart types. While our results suggest that familiar encodings based on visual perception studies can be useful in tactile graphics, our subjects also expressed a desire to use encodings designed explicitly for BLV people. Based on this study, we identify gaps between the perceptual expectations of common charts and the perceptual tools available in tactile perception. Then, we present a set of guidelines for the design of tactile graphics that accounts for these gaps. Supplemental material is available at https://osf.io/3nsfp/?view_only=7b7b8dcbae1d4c9a8bb4325053d13d9f.

Data visualization creators often lack formal training, resulting in a knowledge gap in design practice. Large-language models such as ChatGPT , with their vast internet-scale training data, offer transformative potential to address this gap. In this study, we used both qualitative and quantitative methods to investigate how well ChatGPT can address visualization design questions. First, we quantitatively compared the ChatGPT -generated responses with anonymous online Human replies to data visualization questions on the VisGuides user forum. Next, we conducted a qualitative user study examining the reactions and attitudes of practitioners toward ChatGPT as a visualization design assistant. Participants were asked to bring their visualizations and design questions and received feedback from both Human experts and ChatGPT in randomized order. Our findings from both studies underscore ChatGPT’s strengths—particularly its ability to rapidly generate diverse design options—while also highlighting areas for improvement, such as nuanced contextual understanding and fluid interaction dynamics beyond the chat interface. Drawing on these insights, we discuss design considerations for future LLM-based design feedback systems.

Traditional accessibility methods like alternative text and data tables typically underrepresent data visualization’s full potential. Keyboard-based chart navigation has emerged as a potential solution, yet efficient data exploration remains challenging. We present VizAbility, a novel system that enriches chart content navigation with conversational interaction, enabling users to use natural language for querying visual data trends. VizAbility adapts to the user’s navigation context for improved response accuracy and facilitates verbal command-based chart navigation. Furthermore, it can address queries for contextual information, designed to address the needs of visually impaired users. We designed a large language model (LLM)-based pipeline to address these user queries, leveraging chart data & encoding, user context, and external web knowledge. We conducted both qualitative and quantitative studies to evaluate VizAbility’s multimodal approach. We discuss further opportunities based on the results, including improved benchmark testing, incorporation of vision models, and integration with visualization workflows.

With the growing complexity and volume of data, visualizations have become more intricate, often requiring advanced techniques to convey insights. These complex charts are prevalent in everyday life, and individuals who lack knowledge in data visualization may find them challenging to understand. This paper investigates using Large Language Models (LLMs) to help users with low data literacy understand complex visualizations. While previous studies focus on text interactions with users, we noticed that visual cues are also critical for interpreting charts. We introduce an LLM application that supports both text and visual interaction for guiding chart interpretation. Our study with 26 participants revealed that the in-situ support effectively assisted users in interpreting charts and enhanced learning by addressing specific chart-related questions and encouraging further exploration. Visual communication allowed participants to convey their interests straightforwardly, eliminating the need for textual descriptions. However, the LLM assistance led users to engage less with the system, resulting in fewer insights from the visualizations. This suggests that users, particularly those with lower data literacy and motivation, may have over-relied on the LLM agent. We discuss opportunities for deploying LLMs to enhance visualization literacy while emphasizing the need for a balanced approach.

Data visualization serves as a crucial tool for communicating important information in our society. Yet, as visualizations grow more complex, they become less accessible to individuals with visual impairments. Traditional accessibility approaches like alternative text and data tables often fall short of capturing the full potential of data visualization. To bridge this gap, we introduce VizAbility, a novel multimodal accessible system that combines keyboard navigation with conventional interaction, enabling individuals with visual impairments to actively engage with and explore data visualizations. We built an LLM-based pipeline that classifies user queries and synthesizes underlying data, chart structure, user locality, and web-based information to answer the queries. Our preliminary evaluation using real-world questions from blind individuals demonstrates the significant potential of VizAbility.

Modern visualization software and programming libraries have made data visualization construction easier for everyone. However, the extent of accessibility design they support for blind and low‐vision people is relatively unknown. It is also unclear how they can improve chart content accessibility beyond conventional alternative text and data tables. To address these issues, we examined the current accessibility features in popular visualization tools, revealing limited support for the standard accessibility methods and scarce support for chart content exploration. Next, we investigate two promising accessibility approaches that provide off‐the‐shelf solutions for chart content accessibility: structured navigation and conversational interaction. We present a comparative evaluation study and discuss what to consider when incorporating them into visualization tools.