Avatar is a critical medium for identity representation in social virtual reality (VR). However, options for disability expression are highly limited on current avatar interfaces. Improperly designed disability features may even perpetuate misconceptions about people with disabilities (PWD). As more PWD use social VR, there is an emerging need for comprehensive design standards that guide developers and designers to create inclusive avatars. Our work aim to advance the avatar design practices by delivering a set of centralized, comprehensive, and validated design guidelines that are easy to adopt, disseminate, and update. Through a systematic literature review and interview with 60 participants with various disabilities, we derived 20 initial design guidelines that cover diverse disability expression methods through five aspects, including avatar appearance, body dynamics, assistive technology design, peripherals around avatars, and customization control. We further evaluated the guidelines via a heuristic evaluation study with 10 VR practitioners, validating the guideline coverage, applicability, and actionability. Our evaluation resulted in a final set of 17 design guidelines with recommendation levels.
Computational thinking (CT) is regarded as a fundamental twenty-first century skill and has been implemented in many early childhood education curriculum. Yet, the needs of neurodivergent children have remained largely overlooked in the extensive research and technologies built to foster CT among children. To address this, we investigated how to support neurodiverse (i.e., groups involving neurodivergent and neurotypical) preschoolers aged 3-5 in learning CT concepts. Grounded in interviews with six teachers, we deployed an age-appropriate, programmable robot called KIBO in two preschool classrooms involving 12 neurodivergent and 17 neurotypical children for eight weeks. Using interaction analysis, we illustrate how neurodivergent children found enjoyment in assembling KIBO and learned to code with it while engaging in cooperative and competitive play with neurotypical peers and the adults. Through this, we discuss accessible adaptations needed to enhance CT among neurodivergent preschoolers and ways to reimagine technology-mediated social play for them.
Daytime urinary frequency syndrome (DUFS) is a prevalent pediatric voiding dysfunction. Managing DUFS involves sufficient water intake and monitoring voiding and defecation behaviors, which can be challenging for preschool-aged patients to perform throughout the day for prolonged periods. To address this problem, we created FluidTrack, a semi-automated tracking system enabling child and parents to collaboratively track child's fluid intake, voiding, and defecation, while encouraging adequate water consumption. To examine preschoolers’ engagement in behavior tracking with their parents, we conducted a 4-week deployment study with 14 DUFS patients (4--6 years) and their parents as part of DUFS management. The majority of patient participants enthusiastically engaged in semi-automated data capture, driven by their initial interest in FluidTrack. Sustaining the children’s enthusiasm and behind-the-scenes parental assistance were critical for continuing semi-automated tracking. Our findings demonstrated the feasibility of children’s semi-automated self-tracking in collaboration with their parents, and identified design suggestions for future work.
Despite the growth of video as a medium, videos remain inaccessible to many people. Prior video accessibility research has focused primarily on blind and low vision or d/Deaf and hard of hearing audiences. However, the video watching experiences of people with ADHD are largely unexplored. Through semi-structured interviews with 20 participants self-identifying with ADHD, we uncovered video watching frustrations, current strategies for access, and desired accessibility features. Participants faced both overstimulation and understimulation from visuals and audio (e.g., flashing lights, slower speech), which impacted their attention, engagement, and information retention. Common strategies included altering video speed, using captions, and leveraging timestamps for skipping through videos. Participants desired adjustable sound channels for aiding focus, video summaries for retaining information, and warnings for preempting sensory discomfort. We close by discussing (1) design recommendations for platforms and creators to support users in achieving their viewing goals and (2) ADHD-inclusive design principles.
As minimally verbal autistic (MVA) children communicate with parents through few words and nonverbal cues, parents often struggle to encourage their children to express subtle emotions and needs and to grasp their nuanced signals. We present AACessTalk, a tablet-based, AI-mediated communication system that facilitates meaningful exchanges between an MVA child and a parent. AACessTalk provides real-time guides to the parent to engage the child in conversation and, in turn, recommends contextual vocabulary cards to the child. Through a two-week deployment study with 11 MVA child-parent dyads, we examine how AACessTalk fosters everyday conversation practice and mutual engagement. Our findings show high engagement from all dyads, leading to increased frequency of conversation and turn-taking. AACessTalk also encouraged parents to explore their own interaction strategies and empowered the children to have more agency in communication. We discuss the implications of designing technologies for balanced communication dynamics in parent-MVA child interaction.
Speech-language pathologists (SLPs) provide support to children with speech and language difficulties through delivering evaluation, assessment, and interventions. Despite growing research on how Artificial Intelligence (AI) can support SLPs, there is limited research examining how AI can assist SLPs in delivering equitable care to culturally and linguistically diverse (CLD) children with disabilities. Through interviews with 15 SLPs and a two-part survey study with 13 SLPs, we report on SLP challenges in delivering responsive care to CLD children with disabilities (i.e., unrepresentative materials, unreliable translation, insufficient support for language variations), areas for AI-based support, evaluations of how available AI performs in addressing these challenges, and bias assessments of AI-generated materials. We discuss implications of contextually unaware AI, the range of care in AI-prompting, tensions and tradeoffs of AI-based support, and honoring diverse representations in AI-generated materials. We offer considerations for SLPs using AI-based tools and general-purpose AI in their practice.
Initiating joint attention (JA) is a fundamental first step in social interactions. In sighted individuals, it relies predominantly on visual cues, such as gaze and hand gestures. These features can reduce opportunities for blind and visually impaired (BVI) and sighted people to interact. Understanding the strategies to navigate these challenges is necessary to develop technology that can facilitate more inclusive JA. To address this, we conducted a longitudinal case study of five children with mixed visual abilities engaging in activities rich with JA opportunities. In a teacher-led classroom, the children experimented with the use of an AI-powered headset designed to support BVI people in social situations. Interaction analysis established that situational complexity affects the children’s responses to initiation attempts. Furthermore, the headset adds to this complexity, affecting the frequency and reactions to attempts to initiate JA. The findings informed the creation of a JA initiation framework and suggestions for future design.