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.

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.

Unlike traditional educational chatbots that rely on pre-programmed responses, large-language model-driven chatbots, such as ChatGPT, demonstrate remarkable versatility to serve as a dynamic resource for addressing student needs from understanding advanced concepts to solving complex problems. This work explores the impact of such technology on student learning in an interdisciplinary, project-oriented data visualization course. Throughout the semester, students engaged with ChatGPT across four distinct projects, designing and implementing data visualizations using a variety of tools such as Tableau, D3, and Vega-lite. We collected conversation logs and reflection surveys after each assignment and conducted interviews with selected students to gain deeper insights into their experiences with ChatGPT. Our analysis examined the advantages and barriers of using ChatGPT, students’ querying behavior, the types of assistance sought, and its impact on assignment outcomes and engagement. We discuss design considerations for an educational solution tailored for data visualization education, extending beyond ChatGPT’s basic interface.