2025-05-14 バッファロー大学(UB)
<関連情報>
- https://www.buffalo.edu/news/releases/2025/05/detect-dyslexia-with-AI-powered-handwriting-analysis.html
- https://link.springer.com/article/10.1007/s42979-025-03927-0
AIによる子供の筆跡分析: 失読症と書字障害の早期スクリーニングの枠組み AI-Enhanced Child Handwriting Analysis: A Framework for the Early Screening of Dyslexia and Dysgraphia
Sahana Rangasrinivasan,M. S. Sumi Suresh,Abbie Olszewski,Srirangaraj Setlur,Bharat Jayaraman & Venu Govindaraju
SN Computer Science Published:17 April 2025
DOI:https://doi.org/10.1007/s42979-025-03927-0
Abstract
Dyslexia and dysgraphia are two specific learning disabilities (SLDs) that are prevalent among children. To minimize the negative impact these SLDs have on a child’s academic and social-emotional development, it is crucial to identify dyslexia and dysgraphia at an early age, enabling timely and effective intervention. The first step in this process is screening, which helps determine if a child requires further instruction or a more in-depth assessment. Current screening tools are expensive, require additional administration time beyond regular classroom activities, and are designed to screen exclusively for one condition, not for both dyslexia and dysgraphia, which often share some common behavioral characteristics. Most dyslexia screeners focus on speech and oral tasks and exclude writing activities. However, analyzing children’s writing samples for behavioral signs of dyslexia and dysgraphia can offer valuable insights into the screening process, which can be time-consuming. As a solution, we propose a co-designed framework for building artificial intelligence (AI) tools that could boost the efficiency of screening and aid practitioners such as speech-language pathologists (SLPs), occupational therapists, general educators, and special educators by simplifying their tasks. This paper reviews current screening methods employed by practitioners, the use of AI-based systems in identifying dyslexia and dysgraphia, and the handwriting datasets available to train such systems. The paper also outlines a framework for developing an AI-integrated screening tool that can identify writing-based behavioral indicators of dyslexia and dysgraphia in children’s handwriting. This framework can be used in conjunction with current screening tools like the Dysgraphia and Dyslexia Behavioral Indicator Checklist (DDBIC). The paper also proposes a methodology for collecting children’s offline and online handwriting samples to build a valuable dataset for developing AI solutions. The proposed framework and data collection methodology are co-designed with SLPs, occupational therapists (OTs), special educators, and general educators to ensure the tool can provide explainable, actionable information that would be invaluable in a practical setting.
