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.

Generative AI (AI) has become ubiquitous in both daily and professional life, with emerging research demonstrating its potential as a tool for accessibility. Neurodivergent people, often left out by existing accessibility technologies, develop their own ways of navigating normative expectations. GAI offers new opportunities for access, but it is important to understand how neurodivergent "power users"-successful early adopters-engage with it and the challenges they face. Further, we must understand how marginalization and intersectional identities influence their interactions with GAI. Our autoethnography, enhanced by privacy-preserving GAI-based diaries and interviews, reveals the intricacies of using GAI to navigate normative environments and expectations. Our findings demonstrate how GAI can both support and complicate tasks like code-switching, emotional regulation, and accessing information. We show that GAI can help neurodivergent users to reclaim their agency in systems that diminish their autonomy and self-determination. However, challenges such as balancing authentic self-expression with societal conformity, alongside other risks, create barriers to realizing GAI's full potential for accessibility.

Blind or Low-Vision (BLV) users often rely on audio descriptions (AD) to access video content. However, conventional static ADs can leave out detailed information in videos, impose a high mental load, neglect the diverse needs and preferences of BLV users, and lack immersion. To tackle these challenges, we introduce Spica, an AI-powered system that enables BLV users to interactively explore video content. Informed by prior empirical studies on BLV video consumption, Spica offers interactive mechanisms for supporting temporal navigation of frame captions and spatial exploration of objects within key frames. Leveraging an audio-visual machine learning pipeline, Spica augments existing ADs by adding interactivity, spatial sound effects, and individual object descriptions without requiring additional human annotation. Through a user study with 14 BLV participants, we evaluated the usability and usefulness of Spica and explored user behaviors, preferences, and mental models when interacting with augmented ADs.

Large Language Models (LLMs) have the potential to contribute to the fields of nutrition and dietetics in generating food product explanations that facilitate informed food selections. However, the extent to which these models offer effective and accurate information remains unverified. In collaboration with registered dietitians (RDs), we evaluate the strengths and weaknesses of LLMs in providing accurate and personalized nutrition information. Through a mixed-methods approach, RDs validated GPT-4 outputs at various levels of prompt specificity, which led to the development of design guidelines used to prompt LLMs for nutrition information. We tested these guidelines by creating a GPT prototype, The Food Product Nutrition Assistant, tailored for food product explanations. This prototype was refined and evaluated in focus groups with RDs. We find that the implementation of these dietitian-reviewed template instructions enhance the generation of detailed food product descriptions and tailored nutrition information.

Understanding nutrition labels remains challenging for consumers; however, digital shopping environments offer opportunities to explore how interactive nutrition labels may be used to enhance comprehension. We conducted an A/B study with 24 participants, comparing their ability to interpret and apply nutrition information using conventional, static labels versus interactive labels. We evaluated interactive nutrition labels’ impact through quantitative metrics and qualitative insights from interviews and think-aloud sessions. Our findings reveal a statistically significant improvement in assessing nutrient amounts and interpreting numerical information when users engage with interactive labels. These results underscore the potential interactivity has on promoting public understanding of nutritional content and highlight opportunities for refinement. Based on our findings, we propose new design directions and discuss technology’s role in making nutrition labels more effective for decision-making and nutrition education.

The increasing ubiquity of data in everyday life has elevated the importance of data literacy and accessible data representations, particularly for individuals with disabilities. While prior research predominantly focuses on the needs of the visually impaired, our survey aims to broaden this scope by investigating accessible data representations across a more inclusive spectrum of disabilities. After conducting a systematic review of 152 accessible data representation papers from ACM and IEEE databases, we found that roughly 78% of existing articles center on vision impairments. In this article, we conduct a comprehensive review of the remaining 22% of papers focused on underrepresented disability communities. We developed categorical dimensions based on accessibility, visualization, and human-computer interaction to classify the papers. These dimensions include the community of focus, issues addressed, contribution type, study methods, participants involved, data type, visualization type, and data domain. Our work redefines accessible data representations by illustrating their application for disabilities beyond those related to vision. Building on our literature review, we identify and discuss opportunities for future research in accessible data representations.

To identify technological opportunities to better support nutrition security and equality among those living in low-socioeconomic situations, we conducted 33 semi-structured interviews and seven in-home visits of lower- to middle-income households from a mid-sized city in northern Indiana. Inspired by assets-based approaches to public health, we investigated technology’s role in supporting how participants selected and purchased food, planned meals, and worked through logistical barriers to obtain food. Technology helped participants identify sales and coupons, search for recipes and health-related insights to address diet and health concerns, and share information. We contribute design implications (e.g., amplifying optimization behaviors and social engagement, leveraging substitutions) in support of food agency. We further contribute three emergent archetypes to convey central shopping tendencies (i.e., inventory shoppers, menu planners, and adaptive shoppers) and identify corresponding design implications. We situate our results into nutrition decision-making and education, social psychology, food consumer studies, and HCI literature.