🏷️ Concept
Special Education
Special Education — the design and delivery of instruction for learners with disabilities, spanning cognitive, physical, sensory, and neurodevelopmental differences. AI in education research in this wiki explores how AI tools can support diverse learner needs through personalization, adaptive scaffolding, and accessible interfaces — while also examining the risks of AI systems that overlook or marginalize disabled learners.
Special education is a domain where AI's capacity for personalization and adaptation offers particular promise. Unlike one-size-fits-all instruction, AI tutors can theoretically adapt to individual cognitive profiles, communication needs, and learning paces. The articles in this wiki span AI for specific disability profiles, neurodivergent learner experiences, and critical perspectives on AI and disability.
Disability-specific AI tutoring tailors AI to particular learner needs. Special-R1 extends reinforcement learning to model cognitive and communicative diversity across five disability profiles, using persona-aware prompts and thinking rewards to shape tutor responses for each learner. DysLexLens analyzed how dyslexic learners experience AI tools, revealing both the value of AI for literacy support and persistent accessibility barriers. Embodied String Learning Blindness Low Vision Musicians developed non-visual learning strategies with blind and low-vision musicians, centering disability-led embodied design. These connect to Accessible Learning and Neurodiversity.
Neurodivergent learner experiences center autistic and ADHD students. Zastudil et al. found neurodivergent computing students need structured assignments, small consistent teams, and explicit role definitions — design requirements that Collaborative Learning tools must address. Adhd Video Segmentation Computing Education demonstrated that AI-segmented videos eliminated the ADHD performance gap. Both connect to Instructional Design and Universal Design For Learning.
Critical perspectives examine how AI can marginalize disabled learners. Tali-Otmani argues that AI systems actively marginalize disability-centered knowledge due to Western-centric training data — connecting to Equity In AI Education concerns about epistemic justice.