Tactile graphics present visual information to blind and visually impaired individuals in an accessible way, through touch. Current methods for producing tactile graphics, such as embossing or swell-paper printing, have limitations such as durability - and the tools required to produce them are limited in expressiveness. In this project, we explore embroidery as a medium for producing tactile graphics. Embroidery, traditionally known for its variety and visual beauty, offers not just improved durability and ease of production - but the ability to convey information through a broad range of stitch types. Following an exploration of the design space of embroidered tactile graphics, we identify key perceptual properties that impact how embroidered textures are differentiated. Based on these differences, we introduce an optimization algorithm for assigning textures to regions of tactile graphics in a way that makes them diverse and legible. We implement an end-to-end pipeline for producing embroidered tactile graphics and evaluate the comprehensibility and legibility of our design with 6 blind participants. Our findings showed that embroidered tactile graphics present information accurately and comprehensively, and that measurable properties, such as the use of spacing and distinctiveness, were an important factor of expressive and legible design.
Hypertension, which disproportionately affects older adults, continues to pose a critical health challenge among Indigenous communities, where systemic healthcare inequities and cultural mismatches in medical interventions contribute to high rates of undiagnosed and unmanaged cases. This paper explores the co-design of culturally relevant mobile health games to support hypertension management among Indigenous Peoples in Northern Arizona. Drawing from participatory design methodologies, we conducted a workshop with 14 participants to conceptualize and prototype mobile applications focused on five key factors of hypertension care: medication adherence, nutrition, exercise, education, and blood pressure monitoring. The workshops revealed that culturally rooted play and storytelling may significantly enhance user engagement and acceptance of digital health tools. Using insights from these sessions, we developed five medium-fidelity prototypes that reflect diverse game and play strategies, from traditional gamification techniques to reappropriations of existing games infused with Indigenous cultural elements. Our findings highlight how participatory and culturally grounded design approaches can produce mobile health interventions that may resonate more deeply with Indigenous worldviews and healthcare practices. This study contributes empirical design artifacts and knowledge to the field of accessible health technologies, emphasizing the importance of Indigenous-led co-design in addressing chronic disease disparities. We conclude by outlining future directions for iterative prototyping and playtesting, with implications for broader applications in culturally responsive digital health innovation.
AI-generated audio descriptions (ADs) offer scalable solutions for making visual media accessible to blind and low-vision (BLV) audiences. Nevertheless, little is known about howBLV users experience and evaluate these descriptions across emerging platforms. In this qualitative study, we conducted semi-structured interviews with ten (N=10) BLV participants, recruited based on divergences in prior survey ratings, to explore their perceptions of both human- and AI-generated ADs in contexts ranging from traditional film to shortform video and livestreams. Thematic analysis revealed five key themes: (1) information prioritization and genre-sensitive details, (2) the "better-than-nothing" consensus tempered by emotional and contextual gaps in AI-generated ADs, (3) the social dynamics of shared viewing, (4) accessibility deserts on new media platforms, and (5) the artistry-precision dilemma. Our findings highlight the need for adaptive, transparent, and user-informed AD systems that balance narrative resonance with efficiency. We conclude with design recommendations for co-designing AI-assisted accessibility tools in partnership with BLV communities.
Accessibility research has a broad mandate: use technology to make the world more accessible to disabled people. Yet, as a field, accessibility research lacks a clear characterization of what "accessibility" is. Furthermore, it has been historically limited in who is designed for, focusing on specific types of disability and often failing to consider how disability intersects with other identities. We set out to explicate what it means to make something accessible, grounded in the lived experiences of a diverse group of 25 disabled people. From our empirical findings, we develop a process for modeling accessibility. First, an individual assesses their experience of inaccess, specifically, the type of barrier they face, the technology repertoire they possess, and the contextual factors that shape how they address accessibility barriers. Then, having assessed an access barrier, they perform consequence calculus, weighing all available options to achieve access and deciding upon the option that best matches their priorities. We highlight the situated nature of access; people's identities, contextual factors, repertoires, and priorities all dictate their experience of accessibility.
Sign language processing holds great promise for advancing societal inclusivity, yet it often excludes meaningful participation from the Deaf community, raising ethical and practical concerns about the applicability of AI solutions to their needs. This paper addresses these gaps through two interrelated studies. First, surveys identify differences in priorities and expectations between machine learning (ML) practitioners and Deaf American Sign Language (ASL) signers. Second, paired co-design sessions bring ML and ASL experts together to generate guiding questions that support practices for aligning AI development with community goals. Our findings reveal critical points of friction that reflect deeper systemic and epistemic barriers to effective collaboration. By synthesizing unique and shared insights from both groups, we provide empirically grounded resources to guide collaborative frameworks that promote the agency and expertise of the Deaf community. This research paves actionable pathways toward equitable, communitycentered advancements in AI.
