AI Handwriting Analysis May Catch Dyslexia and Dysgraphia Early

Summary: A new AI-driven tool developed by researchers could revolutionize how educators and clinicians screen for dyslexia and dysgraphia in children. By analyzing handwriting samples from K–5 students, the system detects behavioral cues, spelling errors, motor difficulties, and cognitive issues with remarkable precision. Unlike traditional screening, which is time-intensive and often condition-specific, this method is faster, scalable, and could ease the burden on the nation’s limited speech and occupational therapy workforce. The research underscores the value of using artificial intelligence for early intervention, particularly in underserved communities. Key Facts: Multimodal Detection: The tool analyzes visual, motor, and cognitive elements of handwriting. Early Intervention: It identifies signs of both dyslexia and dysgraphia before major academic setbacks occur. Accessible Screening: Could help address workforce shortages in speech-language pathology and occupational therapy. Source: University at Buffalo A new University at Buffalo-led study outlines how artificial intelligence-powered handwriting analysis may serve as an early detection tool for dyslexia and dysgraphia among young children.