Three-dimensional (3D) data visualizations, such as surface plots, are vital in STEM fields from biomedical imaging to meteorology and spectroscopy, yet remain largely inaccessible to blind and low-vision (BLV) people. To address this gap, we conducted an Experience-Based Co-Design (EBCD) with BLV co-designers with expertise in non-visual data representations to create an accessible, multi-modal, web-native visualization tool. Using a multi-phase co-design methodology, our team of five BLV and one non-BLV researcher(s) participated in two iterative sessions, comparing a low-fidelity tactile probe with a high-fidelity digital prototype. This process produced a prototype with empirically grounded features, including reference sonification, stereo and volumetric audio, and configurable buffer aggregation, which our BLV co-designers validated as improving analytic accuracy and learnability. In this study, we explicitly target core analytic tasks essential for non-visual 3D data exploration: 3D orientation, landmark and peak finding, comparing local maxima versus global trends, gradient tracing, and identifying occluded or partially hidden features. Our work offers accessibility researchers and developers a co-design protocol for translating tactile knowledge to digital interfaces, concrete design guidance for future systems, and opportunities to extend accessible 3D visualization into embodied data environments.

Research touts universal participation through accessibility initiatives, yet blind and low-vision (BLV) researchers face systematic exclusion as visual representations dominate modern research workflows. To materialize inclusive processes, we, as BLV researchers, examined how our peers combat inaccessible infrastructures. Through an explanatory sequential mixed-methods approach, we conducted a cross-sectional, observational survey (n=57) and follow-up semi-structured interviews (n=15), analyzing open-ended data using reflexive thematic analysis and framing findings through activity theory to highlight research’s systemic shortcomings. We expose how BLV researchers sacrifice autonomy and shoulder physical burdens, with nearly one-fifth unable to independently perform literature review or evaluate visual outputs, delegating tasks to sighted colleagues or relying on AI-driven retrieval to circumvent fatigue. Researchers also voiced frustration with specialized tools, citing developers’ performative responses and losing deserved professional accolades. We seek follow-through on research’s promises through design recommendations that reconceptualize accessibility as fundamental to successful research and supporting BLV scholars’ workflows.

Writing mathematical notation requires substantial effort, diverting cognitive resources from conceptual understanding to documentation mechanics, significantly impacting individuals with fine motor disabilities (FMDs).Current limits of speech-based math technologies rely on precise dictation of math symbols and unintuitive command-based interfaces.We present a novel voice-powered math workspace, applying neuroscience insights to create an intuitive problem-solving environment.To minimize cognitive load, we leverage large language models with our novel context engine to support natural language interaction.Ultimately, we enable fluid mathematical engagement for individuals with FMDs-freed from mechanical constraints CCS Concepts• Human-centered computing → Accessibility design and evaluation methods; Accessibility technologies; Accessibility systems and tools; Empirical studies in accessibility.

Blind and low-vision (BLV) individuals experience lower levels of physical activity (PA) due to limited access to engaging, accessible exercise tools.We present PunchPulse, an open-source VR boxing exergame (available on GitHub) designed in collaboration with BLV co-designers to support sustained moderate-to-vigorous physical activity (MVPA) through immersive, autonomous gameplay.Our system emphasizes structured progression and multimodal interaction (e.g., heart-rate tracking, audio-haptic feedback) to scaffold engagement and exertion.Over a seven-month, multi-phased study, PunchPulse was iteratively refined with three BLV co-designers, informed by two early pilot testers, and evaluated by six additional BLV user-study participants.Data collection included both qualitative (researcher observations, semi-structured interviews) and quantitative (MVPA zones, aid usage, completion times) measures of physical exertion and gameplay performance.The user study revealed that all participants reached moderate MVPA thresholds, with high levels of immersion and engagement observed.This work demonstrates the potential of VR as an inclusive medium for promoting meaningful PA in the BLV community and addresses a critical gap in accessible, intensity-driven exercise interventions.

Blind and low-vision (BLV) users remain largely excluded from three-dimensional (3D) surface and point data visualizations due to the reliance on visual interaction. Existing approaches inadequately support non-visual access, especially in browser-based environments. This study introduces DIXTRAL, a hosted web-native system, co-designed with BLV researchers to address these gaps through multimodal interaction. Conducted with two blind and one sighted researcher, this study took place over sustained design sessions. Data were gathered through iterative testing of the prototype, collecting feedback on spatial navigation, sonification, and usability. Co-design observations demonstrate that synchronized auditory, visual, and textual feedback, combined with keyboard and gamepad navigation, enhances both structure discovery and orientation. DIXTRAL aims to improve access to 3D continuous scalar fields for BLV users and inform best practices for creating inclusive 3D visualizations.

For blind and low-vision (BLV) individuals, digital math communication is uniquely difficult due to the lack of accessible tools. Currently, the state of the art is either code-based, like LaTeX, or WYSIWYG, like visual editors. However, both paradigms view math communication as primarily a visual typesetting problem, and may be accessible but difficult to use. In this paper, we present an equation editor that is built from the ground up with BLV accessibility in mind. Specifically, we notice that two of the biggest barriers with current technology are the high cognitive load and the lack of spatial relationships. Thus, we build an editor that uses spatial audio cues, muscle memory, tones, and more intuitive navigation to properly contextualize math equations. We discuss how this new paradigm can enable new levels of math communication, engagement, and literacy. Finally, we discuss natural next steps.

Virtual reality (VR) has been celebrated for its immersive experiences, yet its potential for creating accessible and enjoyable environments for Blind and Low-Vision (BLV) individuals remains underexplored. Our project addresses this gap by developing a VR table tennis game specifically designed for BLV players. Utilizing an autoethnographic approach, our mixed-ability team, including three BLV co-designers, prototyped the game through rapid iterative testing and evaluation over four months. We integrated multi-sensory feedback mechanisms, such as spatial audio, haptic feedback, and high-contrast visuals, to enhance navigation and interaction. Our findings highlight the effectiveness of combining these modalities to create an enjoyable and realistic VR sports experience. However, we also identified challenges, such as the need for balanced sensory feedback to avoid overload. This study emphasizes the importance of inclusive design in VR gaming, offering new recreational opportunities for BLV individuals and setting the stage for future advancements in accessible VR technology.

This paper investigates how blind and low-vision (BLV) users interact with multimodal large language models (LLMs) to interpret data visualizations. Building upon our previous work on the multimodal access and interactive data representation (MAIDR) framework, our mixed-visual-ability team co-designed maidrAI, an LLM extension providing multiple AI responses to users’ visual queries. To explore generative AI-based data representation, we conducted user studies with 8 BLV participants, tasking them with interpreting box plots using our system. We examined how participants personalize LLMs through prompt engineering, their preferences for data visualization descriptions, and strategies for verifying LLM responses. Our findings highlight three dimensions affecting BLV users’ decision-making process: modal preference, LLM customization, and multimodal data representation. This research contributes to designing more accessible data visualization tools for BLV users and advances the understanding of inclusive generative AI applications.

Teaching accessibility is crucial to ensuring that accessibility principles are integrated into the design and adoption of software and other aspects of lives. The existing literature on accessibility education is largely siloed, appearing primarily in computer science and related disciplines. Understanding the similarities and differences in teaching accessibility across different disciplines is vital for enhancing educational effectiveness by leveraging lessons learned from each field. This workshop aims to serve as an interdisciplinary forum for researchers and education practitioners to discuss topics taught, approaches taken, resources utilized, and challenges encountered when teaching accessibility in different disciplines.

This paper investigates new data exploration experiences that enable blind users to interact with statistical data visualizations—bar plots, heat maps, box plots, and scatter plots—leveraging multimodal data representations. In addition to sonification and textual descriptions that are commonly employed by existing accessible visualizations, our MAIDR (multimodal access and interactive data representation) system incorporates two additional modalities (braille and review) that offer complementary benefits. It also provides blind users with the autonomy and control to interactively access and understand data visualizations. In a user study involving 11 blind participants, we found the MAIDR system facilitated the accurate interpretation of statistical visualizations. Participants exhibited a range of strategies in combining multiple modalities, influenced by their past interactions and experiences with data visualizations. This work accentuates the overlooked potential of combining refreshable tactile representation with other modalities and elevates the discussion on the importance of user autonomy when designing accessible data visualizations.

This paper delineates a fruitful collaboration between blind and sighted developers, aiming to augment the accessibility of Visual Studio Code (VSCode). Our shared journey is portrayed through examples drawn from our interaction with GitHub issues, pull requests, review processes, and insider’s releases, each contributing to an improved VSCode experience for blind developers. One key milestone of our co-design process is the establishment of an accessible terminal buffer, a significant enhancement for blind developers using VSCode. Other innovative outcomes include Git Diff audio cues, adaptable verbosity settings, intuitive help menus, and a targeted accessibility testing initiative. These tailored improvements not only uplift the accessibility standards of VSCode but also provide a valuable blueprint for open-source developers at large. Through our shared dedication to promoting inclusivity in software development, we aim for the strategies and successes shared in this paper to inspire and guide the open-source community towards crafting more accessible software environments.

Collaborative document editing tools are widely used in professional and academic workplaces. While these tools provide basic accessibility support, it is challenging for blind users to gain collaboration awareness that sighted people can easily obtain using visual cues (e.g., who is editing where and what). Through a series of co-design sessions with a blind coauthor, we identified the current practices and challenges in collaborative editing, and iteratively designed CollabAlly, a system that makes collaboration awareness in document editing accessible to blind users. CollabAlly extracts collaborator, comment, and text-change information and their context from a document and presents them in a dialog box to provide easy access and navigation. CollabAlly uses earcons to communicate background events unobtrusively, voice fonts to differentiate collaborators, and spatial audio to convey the location of document activity. In a study with 11 blind participants, we demonstrate that CollabAlly provides improved access to collaboration awareness by centralizing scattered information, sonifying visual information, and simplifying complex operations.

Collaborative document editing tools are widely used in both professional and academic workplaces. While these tools provide some accessibility features, it is still challenging for blind users to gain collaboration awareness that sighted people can easily obtain using visual cues (e.g., who edited or commented where and what in the document). To address this gap, we present CollabAlly, a browser extension that makes extractable collaborative and contextual information in document editing accessible for blind users. With CollabAlly, blind users can easily access collaborators’ information, track real-time or asynchronous content and comment changes, and navigate through these elements. In order to convey this complex information through audio, CollabAlly uses voice fonts and spatial audio to enhance users’ collaboration awareness in shared documents. Through a series of pilot studies with a coauthor who is blind, CollabAlly’s design was refined to include more information and to be more compatible with existing screen readers.

Runtime memory faults during production run should be more thoroughly addressed because they severely affect system availability. This paper proposes a method for mitigating memory faults during production runs of deployed software, thereby ensuring normal system operation until patches to fix the faults are delivered. Furthermore, the method helps enhance debugging efficiency by providing accurate on-site fault information used by developers to release timely patches. The core of the method is to offer information tagging to identify runtime faults and a fault survival algorithm to provide differentiated fault mitigation according to the runtime state. We implemented ROPHE on a Linux 2.6 platform and conducted an empirical study of representative Linux applications. The results show that the average fault-handling rate among the applications is 35.75%, whereas the RemOte runtime Protection for High-risk Error (ROPHE) greatly improves capacity to an average of 91.94%. Specifically, the fault-handling rates of the applications ranged widely from 7.32% to 62.96%, while ROPHE provided fault-survival rates in the relatively narrow range of 82.35–97.44%. The experimental results show that the proposed method guarantees the same level of reliability for all applications regardless of their individual fault handling capacity. Copyright © 2016 John Wiley & Sons, Ltd.

ISO (International Standardization Organization) and the IEC (International Electrotechnical Commission) provide numerous standards for software products and processes. Utilizing those standards specific to a project requires some tailoring to meet the development domain. This paper includes: (1) a test process meta-model, which is a test process defined in standards; (2) "a scheme for tailoring processes" following a component-based development customization technique; and (3) AutoTP, an "automation tool for tailoring", which is derived from XML techniques. AutoTP is a tool in which a standard test process automatically generates a tailored test process by means of a methodology and a domain. In addition, the Rational Objectory process is used as an empirical study in this paper.