Visual art facilitates expression, communication, and connection, yet it remains inaccessible to those who are visually-impaired and those who lack the resources to understand the techniques and history of art. In this work, I propose the development of a generative AI model that generates a description and interpretation of a given artwork. Such research can make art more accessible, support art education, and improve the ability of AI to understand and translate between creative media. Development will begin with a formative study to assess the needs and preferences of blind and low vision people and art experts. Following the formative study, the basic approach is to train the model on a database of artworks and their accompanying descriptions, predict sentiments from extracted visual data, and generate a paragraph closely resembling training textual data and incorporating sentiment analysis. The model will then be evaluated quantitatively through metrics like METEOR and qualitatively through Turing tests in an iterative process.

Social virtual reality (VR) has become one of the most popular forms of VR. However, despite years of research on how VR interventions can be useful as diagnostic or therapeutic tools for neurodivergent (ND) users, there has been little examination of how accessible social VR may be for such ND individuals. In this paper, we describe an ongoing user study with participants who self-identify with both autism and ADHD (AuDHD) and also self-identify with facing frequent challenges with social interaction. So far, we have recruited four AuDHD participants; we had each participant briefly explore a world on a popular commercial social VR platform and then reflect on this experience afterward in a longer interview section. Through this process, we uncovered various accessibility challenges in social VR, such as difficulties with navigating social norms or managing certain sensory inputs. We also noted ideas on potential accommodations, like a text-based prompt system that can suggest “appropriate” conversation responses. Our work outlines opportunities to improve the accessibility of social VR for an often-overlooked user group.

With the increasing adoption of social virtual reality (VR), it is critical to design inclusive avatars. While researchers have investigated how and why blind and d/Deaf people wish to disclose their disabilities in VR, little is known about the preferences of many others with invisible disabilities (e.g., ADHD, dyslexia, chronic conditions). We filled this gap by interviewing 15 participants, each with one to three invisible disabilities, who represented 22 different invisible disabilities in total. We found that invisibly disabled people approached avatar-based disclosure through contextualized considerations informed by their prior experiences. For example, some wished to use VR’s embodied affordances, such as facial expressions and body language, to dynamically represent their energy level or willingness to engage with others, while others preferred not to disclose their disability identity in any context. We define a binary framework for embodied invisible disability expression (public and private) and discuss three disclosure patterns (Activists, Non-Disclosers, and Situational Disclosers) to inform the design of future inclusive VR experiences.