As virtual 3D environments become more prevalent, equitable access is essential for blind and low-vision (BLV) users, who face challenges with spatial awareness, navigation, and interaction. Prior work has explored supplementing visual information with auditory or haptic modalities, but these methods are static and offer limited support for dynamic, in-context adaptation. Recent advances in generative AI allow users to query and modify 3D scenes via natural language, introducing a paradigm that offers greater flexibility and control for accessibility. We present RAVEN, a system that enables BLV users to issue queries and modification prompts to improve the runtime accessibility of 3D virtual scenes. We evaluated RAVEN with eight BLV people and six Unity developers, generating empirical insights into how conversational programming can support personalized accessibility in 3D environments. Our work highlights both the promise of natural language interaction—intuitive, flexible, and empowering—and the challenges of ensuring reliability, transparency, and trust in generative AI–driven accessibility systems.

People who are blind or have low vision regularly use their hands to interact with the physical world to gain access to objects’ shape, size, weight, and texture. However, many rich visual features remain inaccessible through touch alone, making it difficult to distinguish similar objects, interpret visual affordances, and form a complete understanding of objects. In this work, we present TouchScribe, a system that augments hand-object interactions with automated live visual descriptions. We trained a custom egocentric hand interaction model to recognize both common gestures (e.g., grab to inspect, hold side-by-side to compare) and unique ones by blind people (e.g., point to explore color, or swipe to read available texts). Furthermore, TouchScribe provides real-time and adaptive feedback based on hand movement, from hand interaction states, to object labels, and to visual details. Our user study and technical evaluations demonstrate that TouchScribe can provide rich and useful descriptions to support object understanding. Finally, we discuss the implications of making live visual descriptions responsive to users’ physical reach.

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.

Blind and visually impaired (BVI) computer science students face systematic barriers when learning data structures: current accessibility approaches typically translate diagrams into alternative text, focusing on visual appearance rather than preserving the underlying structure essential for conceptual understanding. More accessible alternatives often do not scale in complexity, cost to produce, or both. Motivated by a recent shift to tools for creating visual diagrams from code, we propose a solution that automatically creates accessible representations from structural information about diagrams. Based on a Wizard-of-Oz study, we derive design requirements for an automated system, Arboretum, that compiles text-based diagram specifications into three synchronized nonvisual formats-tabular, navigable, and tactile. Our evaluation with BVI users highlights the strength of tactile graphics for complex tasks such as binary search; the benefits of offering multiple, complementary nonvisual representations; and limitations of existing digital navigation patterns for structural reasoning. This work reframes access to data structures by preserving their structural properties. The solution is a practical system to advance accessible CS education.

Figure 1: RAVEN is an interactive system that empowers BLV users to query and modify 3D scenes via natural language.The above image illustrates an example of an accessibility modification: A) A low-vision user types in a modification text.B) The system integrates runtime code generation LLM agent with dynamic scene information and instructions to apply accessibilityenhancing changes at runtime.C) The system compiles LLM-produced code to achieve modification while providing spoken response to the user.

Recent advancements in generative AI (GenAI) leveraging large language models (LLMs) have led to scholarship on how they (e.g., ChatGPT) can help neurodivergent students create customized workarounds and refocus energy.In response to calls for countering ableist narratives of lessening burdens and challenging normative norms that favor neurotypical individuals in prior research, we use interviews (n = 19) to center neurodivergent higher-education students' accounts related to the use, motivation, and vision for LLM-based GenAI tools in academia.While students found the tools helpful, their experiences revealed challenges with integration into tried-and-tested workflows, limited AI literacy support, experimentation, and flattening personality.Drawing on crip time, we illustrate how GenAI tools can reinforce the normative value of productivity, shifting the burden of access-making onto students themselves.We propose three design values, flexibility, adaptability, and self-authenticity, to reimagine GenAI as a partner rather than a tool prioritizing speed and self-sufficiency.

AI coding tools are transforming programming, shifting it from a highly editorial process into a conversational activity.Popular Vibe coding tools such as Replit and Cursor integrate natural language interfaces with traditional development environments.While these tools promise simplicity, increased productivity, and automation, they also introduce new accessibility challenges for blind or visually impaired (BVI) developers.A systematic identification of these accessibility challenges necessitates comprehensive guidelines that account for the complex interactions in these tools.To address this need, we develop accessibility heuristics to assess the accessibility of AI conversational programming tools.Our heuristics combine web accessibility guidelines, best practices to design conversational interfaces, and accessibility needs specific to BVI developers.Our evaluation of three widely used conversational programming tools shows that most accessibility challenges arise from complex keyboard interactions, poor focus management, and insufficient feedback and access to the various actions and output of the tools.

We present MatplotAlt, an open-source Python package for easily adding alternative text to Matplotlib figures. MatplotAlt equips Jupyter notebook authors to automatically generate and surface chart descriptions with a single line of code or command, and supports a range of options that allow users to customize the generation and display of captions based on their preferences and accessibility needs. Our evaluation indicates that MatplotAlt's heuristic and LLM-based methods to generate alt text can create accurate long-form descriptions of both simple univariate and complex Matplotlib figures. We find that state-of-the-art LLMs still struggle with factual errors when describing charts, and improve the accuracy of our descriptions by prompting GPT4-turbo with heuristic-based alt text or data tables parsed from the Matplotlib figure.

Touchpads are used widely to interact with computers, yet they provide minimal utility for screen-reader users. We explore the utility of touchpads as input devices for screen-reader users through the development and preliminary evaluation of Touchpad Mapper. This system maps digital content (i.e., images and videos) to the physical coordinates of a touchpad. We examined two usage scenarios: (1) identification of objects and their relative positioning in an image and (2) controlling a video seek bar and slider with rewinding and fast-forwarding features. We conducted task-based semi-structured interviews with two screen-reader users to assess our system’s performance. The participants reported positive experiences, highlighting that they extracted information faster using our system than the conventional keyboard-only interaction.

With the recent rapid rise in Generative Artificial Intelligence (GAI) tools, it is imperative that we understand their impact on people with disabilities, both positive and negative. However, although we know that AI in general poses both risks and opportunities for people with disabilities, little is known specifically about GAI in particular. To address this, we conducted a three-month autoethnography of our use of GAI to meet personal and professional needs as a team of researchers with and without disabilities. Our findings demonstrate a wide variety of potential accessibility-related uses for GAI while also highlighting concerns around verifiability, training data, ableism, and false promises.

Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted through notebook software such as Jupyter, Datalore and Google Colab, both in academia and industry. While there is extensive research to learn how data scientists use computational notebooks, identify their pain points, and enable collaborative data science practices, very little is known about the various accessibility barriers experienced by blind and visually impaired (BVI) users using these notebooks. BVI users are unable to use computational notebook interfaces due to (1) inaccessibility of the interface, (2) common ways in which data is represented in these interfaces, and (3) inability for popular libraries to provide accessible outputs. We perform a large scale systematic analysis of 100000 Jupyter notebooks to identify various accessibility challenges in published notebooks affecting the creation and consumption of these notebooks. Through our findings, we make recommendations to improve accessibility of the artifacts of a notebook, suggest authoring practices, and propose changes to infrastructure to make notebooks accessible.

COVID-19 accelerated the trend toward remote software development, increasing the need for tightly-coupled synchronous collaboration. Existing tools and practices impose high coordination overhead on blind or visually impaired (BVI) developers, impeding their abilities to collaborate effectively, compromising their agency, and limiting their contribution. To make remote collaboration more accessible, we created CodeWalk, a set of features added to Microsoft’s Live Share VS Code extension, for synchronous code review and refactoring. We chose design criteria to ease the coordination burden felt by BVI developers by conveying sighted colleagues’ navigation and edit actions via sound effects and speech. We evaluated our design in a within-subjects experiment with 10 BVI developers. Our results show that CodeWalk streamlines the dialogue required to refer to shared workspace locations, enabling participants to spend more time contributing to coding tasks. This design offers a path towards enabling BVI and sighted developers to collaborate on more equal terms.

Methods are fundamental to doing research and can directly impact who is included in scientific advances. Given accessibility research's increasing popularity and pervasive barriers to conducting and participating in research experienced by people with disabilities, it is critical to ask how methods are made accessible. Yet papers rarely describe their methods in detail. This paper reports on 17 interviews with accessibility experts about how they include both facilitators and participants with disabilities in popular user research methods. Our findings offer strategies for anticipating access needs while remaining flexible and responsive to unexpected access barriers. We emphasize the importance of considering accessibility at all stages of the research process, and contextualize access work in recent disability and accessibility literature. We explore how technology or processes could reflect a norm of accessibility. Finally, we discuss how various needs intersect and conflict and offer a practical structure for planning accessible research.

We present the first toolkit that equips blind and visually impaired (BVI) developers with the tools to create accessible data displays. Called PSST (Physical computing Streaming Sensor data Toolkit), it enables BVI developers to understand the data generated by sensors from a mouse to a micro:bit physical computing platform. By assuming visual abilities, earlier efforts to make physical computing accessible fail to address the need for BVI developers to access sensor data. PSST enables BVI developers to understand real-time, real-world sensor data by providing control over what should be displayed, as well as when to display and how to display sensor data. PSST supports filtering based on raw or calculated values, highlighting, and transformation of data. Output formats include tonal sonification, nonspeech audio files, speech, and SVGs for laser cutting. We validate PSST through a series of demonstrations and a user study with BVI developers.

The COVID-19 pandemic forced many people to convert their daily work lives to a “virtual” format where everyone connected remotely from their home. In this new, virtual environment, accessibility barriers changed, in some respects for the better (e.g., more flexibility) and in other aspects, for the worse (e.g., problems including American Sign Language interpreters over video calls). Microsoft Research held its first cohort of all virtual interns in 2020. We the authors, full time and intern members and affiliates of the Ability Team, a research team focused on accessibility, reflect on our virtual work experiences as a team consisting of members with a variety of abilities, positions, and seniority during the summer intern season. Through our autoethnographic method, we provide a nuanced view into the experiences of a mixed-ability, virtual team, and how the virtual setting affected the team’s accessibility. We then reflect on these experiences, noting the successful strategies we used to promote access and the areas in which we could have further improved access. Finally, we present guidelines for future virtual mixed-ability teams looking to improve access.

Visual semantics provide spatial information like size, shape, and position, which are necessary to understand and efficiently use interfaces and documents. Yet little is known about whether blind and low-vision (BLV) technology users want to interact with visual affordances, and, if so, for which task scenarios. In this work, through semi-structured and task-based interviews, we explore preferences, interest levels, and use of visual semantics among BLV technology users across two device platforms (smartphones and laptops), and information seeking and interactions common in apps and web browsing. Findings show that participants could benefit from access to visual semantics for collaboration, navigation, and design. To learn this information, our participants used trial and error, sighted assistance, and features in existing screen reading technology like touch exploration. Finally, we found that missing information and inconsistent screen reader representations of user interfaces hinder learning. We discuss potential applications and future work to equip BLV users with necessary information to engage with visual semantics.

In graduate school, people with disabilities use disability accommodations to learn, network, and do research. However, these accommodations, often scheduled ahead of time, may not work in many situations due to uncertainty and spontaneity of the graduate experience. Through a three-person autoethnography, we present a longitudinal account of our graduate school experiences as people with disabilities, highlighting nuances and tensions of situations when our requested accommodations did not work and the use of alternative coping strategies. We use retrospective journals and field notes to reveal the impact of our self-image, relationships, technologies, and infrastructure on our disabled experience. Using post-hoc reflection on our experiences, we then close with discussing personal and situated ways in which peers, faculty members, universities, and technology designers could improve the graduate school experiences of people with disabilities.

Blind and visually impaired (BVI) individuals are increasingly creating visual content online; however, there is a lack of tools that allow these individuals to modify the visual attributes of the content and verify the validity of those modifications. In this poster paper, we discuss the design and preliminary exploration of a multi-modal and accessible approach for BVI developers to edit visual layouts of webpages while maintaining visual aesthetics.

Visually impaired people (VIPs) face significant usability and privacy challenges using digital finance technologies. In this paper, we focus on investigating these challenges in the context of Automated Teller Machines (ATMs) in India. To find out the accessibility status of ATMs across India, we first reach out to public banks, and then conduct in-person field surveys and an online crowd sourcing survey of 107 ATM machines across 4 cities in India. We find that less than 18% of surveyed machines are accessible, and follow up with 22 interviews with VIPs regarding challenges using ATMs. Based on insights, we design PocketATM: a system that enables VIPs to use ATMs using their own smartphones - a user can pre-authorize a cash withdrawal using a phone application, then go to any nearby ATM to receive the pre-authorized amount. Our usability evaluation with 19 VIPs demonstrates that PocketATM is usable, practical, and could be embraced by VIPs in India.

In recent times, programming environments like Visual Studio are widely used to enhance programmer productivity. However, inadequate accessibility prevents Visually Impaired (VI) developers from taking full advantage of these environments. In this paper, we focus on the accessibility challenges faced by the VI developers in using Graphical User Interface (GUI) based programming environments. Based on a survey of VI developers and based on two of the authors' personal experiences, we categorize the accessibility difficulties into Discoverability, Glanceability, Navigability, and Alertability. We propose solutions to some of these challenges and implement these in CodeTalk, a plugin for Visual Studio. We show how CodeTalk improves developer experience and share promising early feedback from VI developers who used our plugin.