As generative AI (GenAI) is integrated into everyday technologies, it offers new accessibility opportunities and risks for disabled people. However, little is known about how disabled people navigate GenAI in their everyday lives, particularly how trust, privacy, and intersectional identities affect these experiences. We present findings from seven cross-disability focus groups (N=20) that explore how disabled people navigate GenAI. Our findings reveal that while GenAI supports autonomy, efficiency, and communication, it also introduces accessibility taxes and ethical dilemmas. Although participants voiced skepticism, many continued using GenAI out of necessity. Finally, we found identity-based benefits and tensions, in which GenAI preserved and validated intersecting identities, but also misrepresented and erased those identities. We frame these negotiations as a constant balancing act between access and risk, urging research to further examine how "access" is conceptualized. We offer implications for creating GenAI tools that are transparent, trustworthy, and responsive to intersectional identities.

Research on understanding and supporting the experiences of people with noise sensitivity (PWNS) and their challenges is limited within HCI. Therefore, we build on prior work to understand the challenges they consider and what technological solutions they create to support them. Through eight participatory design workshops involving PWNS and their carers, we considered their needs and challenges and how technology can be designed to support their well-being. Results indicate that wearable and mobile technology can facilitate awareness of sensory triggers and impacts on their well-being. Further, enabling both self and collaborative regulation is also necessary, especially as end users seek independence or interdependence with those around them to manage their experiences. We identified three tensions for designing technology to support PWNS and their sensory experiences.

Noise sensitivity results in a range of experiences, from small annoyances to extreme pain in response to sound. While anyone can experience it, many neurodivergent people report this sensory processing challenge. Novel technologies can help people with noise sensitivity (PWNS) manage their sensitivities. We conducted an interview study with 21 PWNS and 9 caregivers over three months to understand their management and regulation practices and opportunities for technology-mediated support. We found that awareness is a salient factor that influences both management and regulation practices. Based on their awareness, people primarily use avoidance and sound masking approaches or engage in personal coping strategies such as journaling, setting boundaries, and alerting others to regulate. We build on prior research on interdependence, mutual care, and collaborative work to situate the concept of joint awareness to foster collaborative management and regulation for noise sensitivity. Then, we discuss the role of awareness in noise sensitivity management and regulation and provide design principles for the creation of future technology to support noise sensitivity awareness, management, and regulation.

Children who are noise sensitive may be easily distracted, experience discomfort, and react with negative emotions in the presence of loud or sudden sounds and in environments where they cannot control the noise; this can impact their ability to regulate their emotions. Therefore, implementing effective regulation strategies is necessary to support noise-sensitive children. Assistive technology has been shown to be an effective tool for supporting children and self-regulation. By conducting co-design sessions with noise-sensitive children, we will better understand how we might design an assistive application to support emotion regulation in noise-sensitive children. In this work in progress, we present the initial findings of two pilot co-design sessions and considerations for conducting co-design sessions with children who are noise sensitive.

Noise sensitivity is challenging for many people and particularly prevalent for autistic people. In an effort to understand these experiences from the perspective of autistic people and the opportunities for assistive technologies to support noise-sensitive people, we analyzed the posts and responses of noise-sensitive autistic people and their allies in two online forums. These forums were intentionally chosen for their inclusion and centering of autistic people’s experiences as we strove to understand this space from their perspective. Our analysis of both original discussion forum posts about noise sensitivity and comments in response to them reveal three themes: sharing experiences of noise sensitivity, managing noise sensitivity, and disclosing autism. Our work contributes a foundational understanding of the lived experiences of autistic people who are noise-sensitive and the implications for the design of assistive technology to support noise sensitivity.

Some neurodivergent people prioritize visual details over the “big picture”. While excellent attention to detail has many advantages, some contexts require the rapid integration of global and local information. A local processing style can be so strong that local details interfere with the fluid integration of global information required for processing of information rapidly displayed on user interfaces. This disconnect between context of an interaction and processing style can be termed local interference. Personalization of visual stimuli can promote a more accessible computing experience. We describe how technological interventions can support shifting of visual attention from local to global features to make them more accessible. We present two empirical studies. One study with one autistic adult revealed a significant shift in eye gaze fixation, and the other study with 20 autistic children revealed filters that visually emphasize primary aspects encouraged more global comments about the image content.

Although the number of blind or low vision (BLV) software developers is the largest minority population of developers with physical disabilities, they are often marginalized in mainstream online programming communities. We studied how BLV developers engage with a BLV-specific programming community called Program-L, by exploring the help-seeking behaviors of novices. We analyzed 173 messages written by 20 novices over a 4-year period and identified the kinds of help they asked for and their justifications for requesting that help. We learned that self disclosure, practical assistance, and community dynamics were all critical activities to support four types of novices: community, domain, programming, and accessibility. The findings of our work give insight into what support can look like for online communities for marginalized software developers.

People living with dementia as well as dementia caregivers are finding new ways to provide and receive support online. The emerging use of social media platforms-such as TikTok, Clubhouse, and Facebook-by people with dementia and dementia caregivers enables new forms of sharing about stigmatized, poorly understood, and often under-discussed experiences. Building on prior work focused on formal support groups, we report findings from an interview study of how people living with dementia and dementia caregivers provide and receive social support through emerging online spaces and communities. Our analysis reveals that both groups cultivate new online spaces for sharing about the experience of dementia while also navigating tensions surrounding public disclosure of lived experiences, advocacy, education, and the ability to protect and keep private elements of life experiences. We discuss how these groups cultivate online spaces across platforms and contribute to ongoing ethical debates about consent in online communities. We conclude with questions that guide future work on emerging online spaces for supporting people living with dementia and caregivers of people living with dementia.

Social computing provides a variety of challenges and opportunities for people who are experiencing cognitive decline. Following a recent diagnosis of dementia, older adults sometimes engage in online communities designated for people with dementia. In this work, we analyzed all original posts from a seven-year period on a forum for persons with dementia to understand 1) who posts in this forum and 2) what kinds of social support posters seek to gain from this engagement. Our analyses indicate that, in addition to people with dementia, a variety of dementia-related stakeholders create original posts in this forum. Our results suggest that seeking and offering social support are key activities for all forum users, not just those with dementia for whom the forum was designated. Given these findings, we offer design implications for online spaces for vulnerable communities, with a focus on design that allows for external stakeholder participation while still maintaining the privacy and safety of vulnerable members of the community.

Alt text (short for "alternative text") is descriptive text associated with an image in HTML and other document formats. Screen reader technologies speak the alt text aloud to people who are visually impaired. Introduced with HTML 2.0 in 1995, the alt attribute has not evolved despite significant changes in technology over the past two decades. In light of the expanding volume, purpose, and importance of digital imagery, we reflect on how alt text could be supplemented to offer a richer experience of visual content to screen reader users. Our contributions include articulating the design space of representations of visual content for screen reader users, prototypes illustrating several points within this design space, and evaluations of several of these new image representations with people who are blind. We close by discussing the implications of our taxonomy, prototypes, and user study findings.

Autism Spectrum Disorders (ASD) are characterized by deficits in social communication skills, such as response to joint attention (RJA). Robotic systems have been designed and applied to help children with ASD improve their RJA skills. One of the most important goals of robot-assisted intervention is helping children generalize social interaction skills to interact with other people. Thus predicting children's human-human interaction (HHI) performance based on their human-robot interaction (HRI) process is an important task. However, to the best of our knowledge, little research exists exploring this topic. The Early Social-Communication Scales (ESCS) test is a measurement of nonverbal social skills, including RJA, for young children. We conducted two longitudinal user studies with a robot-mediated RJA system in young children with ASD, followed by HHI sessions consisting of ESCS administration. In this paper, we present findings regarding how to predict participants' RJA performance in HHI based on their head pose patterns in HRI, under a semi-supervised machine learning framework. As a three-class classification problem, we achieved a micro-averaged accuracy of 73.5%, which indicates the potential effectiveness of the proposed method.