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Data visualization has long been recognized and applied as a powerful tool for education. However, systematic reviews within the visualization community focusing on this specific area remain limited, and existing studies have focused more on higher education or early childhood education. In this work, we focus on a particular stage of education: teenage education, where students are undergoing significant changes in cognitive abilities and facing increased curriculum demands, making visualization a crucial support tool. We are particularly interested in two research questions: (i) What is the existing method space of implementing visualization in teenage teaching, and (ii) How are the effects of the implemented methods? Driven by these questions, we reviewed 112 papers that provide valuable empirical evidence on how data visualization has been applied in teenage education and analyze three main aspects: (i) visualization applications (e.g., visualization types, forms, tools, educational goals); (ii) its pedagogical integration with curricula and teaching activities; and (iii) how it affects learning performance and user experience. Based on our analysis, we discuss a set of key trends, such as a predominant focus on STEM disciplines and the increasing integration of AR/VR and AI technologies with visualization. We also highlight various implementation challenges, such as the gaps between professional visualization tools and the actual needs of educational settings, as well as the complexity of student characteristics. Through this review, we hope to provide visualization researchers, developers, and educators with in-depth insights to better align the use of visualization with the developmental needs of teenage learners.

The term “visualization design” encompasses multiple concepts and skills that go well beyond current assessments of graphical perception and visualization literacy. In the context of education, what exactly should a student be able to do if they “know” visualization design? To answer this question, we draw on existing methodology from the field of education to propose a concept inventory for visualization design, i.e., a theoretical model capturing the most important concepts and skills commonly associated with visualization design. We initially draft the concept inventory using a qualitative analysis of course objectives from visualization course syllabi. Then, we iteratively refine the concept inventory by soliciting feedback from instructors through semi-structured interviews. Based on our experiences in developing the concept inventory, we reflect on open questions and future research directions in visualization education, such as developing assessments for visualization design (similar to those for visualization literacy) and providing automated assistance for learning and teaching visualization design. Our supplemental materials are available at https://osf.io/dma3f/.

Visualization literacy is a multifaceted construct encompassing skills and competencies, such as decoding data, constructing charts, and identifying design flaws. Yet, assessments of these competencies has been primarily constrained to multiple choice assessments that target lower-order skills, such as chart comprehension. As a result, they often exhibit ceiling effects (i.e., even modestly skilled individuals commonly score near the top of the scale), and do not provide enough information about an individual's higher-order skills (e.g., applying external knowledge, formulating critiques, and designing visualizations). To close these gaps, we develop and investigate two web-based qualitative assessments for testing the critique and design aspects of visualization literacy through online think-aloud critique and sketching of visualization designs based on data and a prompt. We compare performance on our assessments to two established visualization literacy assessments, CALVI and Mini-VLAT, by administering them to three groups that represent three experience levels: crowdworkers, students who have taken a relevant course, and researchers. We find that our critique and sketching assessments capture skills distinct from existing measures and that they differentiate between experienced individuals better than multiple choice-based alternatives. Although administering and grading qualitative assessments can be challenging, our findings suggest qualitative, multimodal assessments are a promising complement to existing visualization literacy assessments, in particular when high visualization skills need to be distinguished.

Data visualizations are a primary means of communicating statistical information to the public. Their expanding use across news media, public health, education, and government reporting places greater importance on audiences' ability to recognize and interpret them. While a significant body of prior research has established frameworks for the measurement of graph and visualization literacy, far less is known about everyday exposure to different kinds of charts and graphs among the general adult population. This gap limits our ability to meaningfully interpret differences in graph literacy, focus efforts on improving that literacy, and design visualizations that align with audience ability. Establishing population level estimates of exposure to data visualizations is therefore essential for improving visual communication and reducing misinterpretation of quantitative information. To address this gap, we surveyed a nationally representative sample of 1,168 U.S. adults via NORC's AmeriSpeak panel about their exposure to sixteen different common data visualization types. The resulting survey responses demonstrate pronounced differences in baseline exposure to data visualizations across chart types and demographic groups. While widely used formats such as bar, pie, line, and grouped bar charts are highly recognizable to the vast majority of U.S. adults, many other practitioner-favored chart types remain unfamiliar to substantial portions of the adult population. Exposure also differs significantly by age and education, with higher educational attainment linked to greater exposure and older adults reporting lower exposure overall. Our findings provide essential population level context on visualization literacy and underscore the importance of aligning data visualization design with audience exposure and experience.

Composite visualizations integrate multiple visualizations in a meaningful manner and show strong expressiveness in representing complex dataset. They have been widely proposed by visualization developers in different applications. However, they often impose a high initial cognitive load on novice users, due to their intrinsic composite designs. Existing visualization onboarding approaches have attempted to mitigate this issue, but they are typically platform-dependent, require substantial manual authoring effort, and struggle with the structural complexity of composite visualizations, making them unable to be used for general composite visualizations. In this paper, we present VizPilot, a novel automated visualization onboarding approach that reverse-engineers composite visualization structure to enable an interactive onboarding experience directly from raw visualization artifacts. The proposed approach consists of two primary modules: the Composite Visualization Analyzer and the Onboarding Interface. Leveraging Multimodal Large Language Models (MLLMs), the Analyzer employs a two stage pipeline: The Semantic Inference stage decomposes the visualization into individual visual components and extracts structured knowledge using a five category taxonomy to generate atomic explanation units, and the Semantic Mapping stage anchors these explanations to precise SVG elements via hierarchical DOM traversal, enabling reliable highlighting and interactions. We implement VizPilot as a browser extension. To initiate the workflow, a visualization developer provides a brief description and optional interaction source code and VizPilot then automatically extracts the SVG and generates bitmap image to drive the multimodal reasoning. Then, the interactive Onboarding Interface will show the structured outputs from the Composite Visualization Analyzer, which supports both Narrative Scrollytelling, providing guided, step-by-step explanations with synchronized highlighting, and Free Exploration for on-demand inquiry by users. We extensively evaluate VizPilot through a comparative analysis of different input modalities to assess the pipeline robustness, a usage scenario to illustrate reduced authoring effort, and a user study to confirm its effect in reducing users’ cognitive load. The overall results demonstrate the effectiveness and usability of VizPilot in facilitating an easy onboarding of composite visualizations.

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.

Visualization style guides play a crucial role in shaping how data is interpreted and trusted, yet they often receive little scrutiny in their creation and use. Understanding their impact requires looking beyond the specific rules that style guides prescribe and examining how they function within organizations to coordinate visual work, manage trade-offs, and support judgment under real constraints. Analyzing interviews with nine authors of twenty-six style guides across journalism, government, industry, and the public sector, we reveal how these guides reflect the specific challenges of their organizations, including consistency, training, governance, and accountability. Our study highlights the tensions between standardization and flexibility, guidance and discretion, and automation and human oversight. We propose PRISM, a socio-technical framework that characterizes visualization style guides by their Purpose, Rules & Mechanisms, Institutional Enforcers, and Situated Flexibility. We show that publicly available style guides expose only a subset of this system. By viewing style guides as socio-technical systems, we enrich the research on design guidance and offer practical insights for those who create and rely on these guides in critical environments.