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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. 

Purpose:Complex scientific problems, including those facing the discipline of communication sciences and disorders (CSD), require interdisciplinary teams of scientists who bring diverse perspectives, knowledge, and skills. According to a recent survey, team science is not yet widely practiced by CSD researchers. This viewpoint describes a current interdisciplinary team science project that addresses a challenging problem for CSD practitioners: meeting the needs of young children with speech and language disabilities for screening and intervention using artificial intelligence–augmented technologies. Method:The article draws from the research literature on the science of team science to describe common challenges faced by interdisciplinary teams and recommended practices to resolve the challenges. Throughout, we provide examples from the National Artificial Intelligence Institute for Exceptional Education to illustrate team science challenges and how they can be addressed. Conclusions:Readers are encouraged to embrace interdisciplinary teamwork to advance the science of CSD. We recommend seeking out training in team science, advocating for professional development opportunities, and institutional support for team science to maximize its benefits for the field. 

Speech-language pathologists are familiar with eligibility criteria for school-based special education services under IDEA, the Individuals with Disabilities Education Act. In order for children with speech and language disorders to be eligible for services, they need to fit one of the thirteen categories of disabilities. However, these 13 categories do not always align well with current evidence-based diagnoses of neurodiverse conditions. It is because of these challenges that we, as members of the National Artificial Intelligence Institute for Exceptional Education, are particularly grateful to the US Department of Education's Office of Special Education programs for issuing new guidance on the use of DLD to accurately describe the speech and language needs of individual children, no matter what eligibility category they fall into. We are also grateful to members and leaders of the American Speech-Language-Hearing Association for their strong advocacy to raise the community's awareness of this new guidance. Therefore, our Institute will be another strong advocate for children with DLD so that they can eventually benefit from our Institute's research. We believe that the recognition of DLD as a disability can greatly help these children and their families.