Articles
Original articles on digital accessibility, research, and practice.
4 results found tagged neurodiversity.
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Robots and Disability: What 36 Accessibility Studies Tell Us
A survey of 36 peer-reviewed accessibility research papers on robotics and disability — spanning the full CaBot / AI Suitcase navigation programme from Chieko Asakawa and colleagues, physical collaboration for blind users, gaze-controlled manipulation, feeding robots, telepresence in classrooms and museums, socially assistive robots (SARs) for older adults, adults with intellectual disabilities, AAC users, and perinatal depression screening, neurodivergent design with robot dogs and swarm robots, BMI-controlled performance, dynamic tactile graphics, a tabletop humanoid that teaches posture to blind learners, and Jang et al.'s theoretical reframing from autonomy to relational sovereignty.
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Trends in Digital Accessibility Research: What Researchers Are Investigating, Finding, and Flagging (2020-2025)
Six years of peer-reviewed accessibility scholarship reveal shifting priorities, emerging populations, and uncomfortable gaps. This article examines trends in what accessibility researchers are choosing to study — from generative AI and neurodivergent experiences to intersecting identities and the Global South — what they are finding, and what they say is still missing.
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Neurodivergence and Digital Accessibility: What Researchers Are Finding
Recent accessibility scholarship is investigating ADHD, autism, and neurodiversity with a breadth and critical depth that challenges how the field thinks about disability itself. This article examines what researchers have found across 88 papers — from biased measurement instruments and the failures of AI career chatbots to how ADHD students build collaborative access through body doubling and how autistic livestreamers find social connection through asymmetric platform dynamics.
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Machine Learning and Digital Accessibility: What Works, What Fails, and What Gets Lost in Between
AI is simultaneously the most promising tool and the most documented source of harm in recent accessibility research. This article examines approximately 235 papers revealing where machine learning helps — captioning accuracy improved by 5.6%, web navigation 52% faster, audio description production halved — and where it fails: fabricating image descriptions, reproducing ableist stereotypes in career chatbots, homogenising personalised AAC tools, and systematically excluding anyone whose body or speech falls outside training data norms.