Artificial intelligence to help people with dyslexia in education: An interdisciplinary literature review

Synthesis: Artificial intelligence to help people with dyslexia in education: An interdisciplinary literature review — Dabaghi, D'Urso & Sciarrone (2026) present a systematic, interdisciplinary review (PRISMA-guided, 2018–2024, n=72 studies) of how AI and generative AI support students with dyslexia in education. They find AI is used for detection, assistive support, and personalized learning, yet the evidence base is fragmented with limited experimental validation, and GAI — despite promising potential for content generation and interactive support — remains minimally represented. The review maps research trends, identifies open challenges, and outlines future directions for inclusive, AI-supported learning for learners with dyslexia.

Key Findings

• AI spans detection, assistive support, and personalized learning for dyslexia, but these strands evolve in parallel rather than in integration; the field is driven more by technological opportunity than by consolidated educational theory, with technical sophistication rarely matched by pedagogical embedding or validated instructional impact.
• Generative AI is under-utilized in this domain. GAI research (all from 2024) clusters into intelligent chatbots, teacher training support, and exploratory studies, and is rapidly overtaking classical ML as the tool of choice — yet rigorous experimentation and real-world validation remain largely absent.
• ML-based help-education tools fall into five areas — specific applications (e.g., music, numbers), engagement, personalization, recommendation, and generic support — but studies emphasize technical performance and classification accuracy while overlooking ecological validity and practical classroom deployment.
• Detection research (EEG, eye-tracking, ML models) prioritizes early intervention and shows diagnostic promise, yet these tools often require specialized equipment and controlled environments, limiting scalability and Accessibility in typical school settings.
• Open challenges include limited experimental validation, scalability and accessibility of diagnostic tools, ethical and Privacy concerns with sensitive student data, limited teacher support and training, and language/cultural barriers (most research targets English-speaking populations).
• Future trends point to GAI-powered personalized materials and real-time adaptive feedback, multi-modal diagnostic models integrating eye-tracking, EEG, and behavioral analytics, NLP-driven intelligent tutoring systems and conversational agents, educator-facing support tools, and interdisciplinary collaboration across AI, education, cognitive science, and psychology.
• Methodological limitations of the review itself include interpretative classification bias, exclusion of non-English studies, heterogeneous evaluation protocols that prevent quantitative synthesis, and a rapidly evolving GAI evidence base that remains preliminary.

Connected Concepts

• Neurodiversity
• Special Education
• Inclusive Learning
• Assistive Technology
• Generative AI
• Accessibility
• Meta-Analysis and Systematic Review
• Personalized Learning

Connected Articles

• Cognitive Offloading in the Age of Generative AI: What Does It Mean for Students With Learning Disabilities?
• Artificial Intelligence in Science and Chemistry Education: A Systematic Review
• A meta-analysis of the effect of generative AI on productivity and learning in programming
• LLM-generated formative feedback in education: A qualitative systematic literature review
• AI Literacy Interventions in Education: A Meta-Analysis of Effects and Moderators

Citation

Dabaghi, K., D'Urso, S., & Sciarrone, F. (2026). Artificial intelligence to help people with dyslexia in education: An interdisciplinary literature review. International Journal of Artificial Intelligence in Education, 36, 100012.