Human-Centered Design, Accessibility & Interactionbookmark_border

Augmented reality has great potential for embedding data visualizations in the world around the user. While this can enhance users’ understanding of their surroundings, it also bears the risk of overwhelming their senses with a barrage of information. In contrast, calm technologies aim to place information in the user’s attentional periphery, minimizing cognitive load instead of demanding focused engagement. In this article, we explore how visualizations can be harmoniously integrated into our everyday life through augmented reality, progressing from visual analytics to ambient analytics.

Research has shown that an audience’s age impacts their engagement in digital media. Interactive narrative visualization is an increasingly popular form of digital media that combines data visualization and storytelling to convey important information. However, audience age is often overlooked by interactive narrative visualization authors. Using an established visualization engagement questionnaire, we ran an empirical experiment where we compared end-user engagement to audience age. We found a small difference in engagement scores where older age cohorts were less engaged than the youngest age cohort. Our qualitative analysis revealed that the terminology and overall understanding of interactive narrative patterns integrated into narrative visualization were more apparent in the feedback from younger age cohorts relative to the older age cohorts. We conclude this article with a series of recommendations for authors of interactive narrative visualization on how to design inclusively for audiences according to their age.

Developing literacy with unfamiliar data visualization techniques, such as parallel coordinate plots (PCPs), can be a significant challenge for students. We adopted the Revised Bloom’s taxonomy to instruct students on PCPs using Mastery Learning in the classroom. To evaluate Mastery Learning’s impact, we conducted an intervention in a data visualization course to teach students about PCPs with and without Mastery Learning. Based on our intervention, we found that while students in both groups performed similarly on the first two (Remember, Understand) modules, the students in the Mastery Learning group performed better on modules that required more advanced thinking (Analyze, Evaluate) and demonstrated a better comprehension of PCPs. We provide all the materials developed including a new, six-module Bloom's Taxonomy PCP literacy (BTPL) test for full reproducibility on our website at https://vis-graphics.github.io/PCP-Literacy-Test/.

Object recognition is a fundamental challenge in computer vision, particularly for fine-grained object classification, where classes differ in minor features. Improved fine-grained object classification requires a teaching system with numerous classes and instances of data. As the number of hierarchical levels and instances grows, debugging these models becomes increasingly complex. Moreover, different types of debugging tasks require varying approaches, explanations, and levels of detail. We present MuCHEx, a multimodal conversational system that blends natural language and visual interaction for interactive debugging of hierarchical object classification. Natural language allows users to flexibly express high-level questions or debugging goals without needing to navigate complex interfaces, while adaptive explanations surface only the most relevant visual or textual details based on the user’s current task. This multimodal approach combines the expressiveness of language with the precision of direct manipulation, enabling context-aware exploration during model debugging.

The emergence of generative AI, large language models, and foundation models is fundamentally reshaping computer science, and visualization and visual analytics are no exception. We present a systematic framework for understanding how human-centered AI (HCAI) can transform the visualization discipline. Our framework maps four key HCAI tool capabilities—amplify, augment, empower, and enhance—onto the four phases of visual sensemaking: view, explore, schematize, and report. For each combination, we review existing tools, envision future possibilities, identify challenges and pitfalls, and examine ethical considerations. This design space can serve as an R&D agenda for both visualization researchers and practitioners to integrate AI into their work as well as understanding how visualization can support HCAI research.

Accessible design for some may still produce barriers for others. This tension, called access friction, creates challenges for both designers and end-users with disabilities. To address this, we present the concept of softerware, a system design approach that provides end-users with agency to meaningfully customize and adapt interfaces to their needs. To apply softerware to visualization, we assembled 195 data visualization customization options centered on the barriers we expect users with disabilities will experience. We built a prototype that applied a subset of these options and interviewed practitioners for feedback. Lastly, we conducted a design probe study with blind and low-vision accessibility professionals to learn more about their challenges and visions for softerware. We observed access frictions between our participant’s designs, and they expressed that for softerware’s success, current and future systems must be designed with accessible defaults, interoperability, persistence, and respect for a user’s perceived effort-to-outcome ratio.