Despite growing attention to autism in technology design, the unique communication style of gestalt language processing, where language is acquired as holistic chunks rather than discrete words, remains largely overlooked in HCI and educational technologies.This single case study examines the technology needs of a young autistic child who communicates using gestalt language processing.Over five months, the child's caregiver kept a reflective journal documenting technology interactions that revealed three key design mismatches between existing technologies and the child's communication preferences.We found that mainstream and social stories often lack emotional balance; language apps overlook the developmental trajectory of gestalt learners including pronoun reversal; and reward-based feedback in educational games misaligns with the child's play motivations.These findings highlight the need for storytelling tools with adjustable emotional arcs, language apps that help children decompose and recontextualize scripts, and feedback systems focused on engagement rather than rewards and competition.These insights inform inclusive design approaches that recognize and support diverse autistic communication styles currently underserved by existing technologies.

Incorporating accessibility education into undergraduate computer science (CS) programs is essential for preparing future technology professionals to create inclusive technology. However, many CS programs lack accessibility coverage, often confining it to human-computer interaction (HCI) courses. To address this gap, we developed accessibility assignments seamlessly integrated into core CS courses. We collaborated closely with ten instructors to select and customize these assignments to suit their needs. To evaluate the impact of these assignments, we conducted interviews with instructors and administered surveys and interviews with their students. Our findings indicate significant improvement in students' familiarity with accessibility concepts and confidence in implementation following completion of the assignments. However, their mindset and future interest in accessibility remained the same. Instructors found it straightforward to incorporate these assignments without compromising core computing concepts. In sum, we validated a foundation for effectively resourcing instructors with accessibility teaching materials and increasing their capacity in accessibility knowledge.

Alternative (alt) text is essential for the accessibility of images to screen reader users. Alt text can be authored by end users or generated automatically using machine learning. Automated methods, while fast and cheap, do not result in high quality alt text. Meanwhile, the large number of images with no alt text on social media indicates that many users do not author alt text for the images they upload. Little is known about whether design elements that motivate and support users are integrated into alt text user interfaces (UIs). We reviewed the current state of alt text UIs in literature, and characterized their design according to behavior change and accountability design factors. We found that the majority of reviewed UIs contained general prompts with no specific motivator or guidance. Many of the UIs did not incorporate design elements to encourage accountability or behavior change. We recommend that future alt text authoring tools incorporate design elements which decrease the user’s cognitive effort, employ motivational and well-timed triggers, and leverage user accountability through alt text visibility and its association with the user’s social presence.

The involvement of individuals with disabilities in online discussions related to disability and accessibility is a critical area of study. While previous research has qualitatively examined the participation of individuals with disabilities on social media platforms, large-scale analysis of social media content by people with disabilities has been an underexplored area. This paper presents a pioneering large-scale study of disability communities on Reddit. We developed an anonymized text-based dataset that consists of 1.5 million comments posted on three subreddits: r/disability, r/Blind, and r/ADHD. Using topic modeling, we analyzed the dataset and identified eight highly-coherent common categories and their associated keywords across the three subreddits. We contribute an Anonymized Disability Discourse Reddit Corpus (ADDReC) of 1.5 million comments that feature eight disability discourse categories.