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Feng, Luxi; Hancock, Roeland; Watson, Christa; Bogley, Rian; Miller, Zachary A.; Gorno-Tempini, Maria Luisa; Briggs-Gowan, Margaret J.; Hoeft, Fumiko – Journal of Learning Disabilities, 2022
Several crucial reasons exist to determine whether an adult has had a reading disorder (RD) and to predict a child's likelihood of developing RD. The Adult Reading History Questionnaire (ARHQ) is among the most commonly used self-reported questionnaires. High ARHQ scores indicate an increased likelihood that an adult had RD as a child and that…
Descriptors: Test Construction, Questionnaires, Artificial Intelligence, Adults
Justice, Laura M.; Ahn, Woo-Young; Logan, Jessica A. R. – Journal of Learning Disabilities, 2019
In this study, we identified child- and family-level characteristics most strongly associated with clinical identification of language disorder for preschool-aged children. We used machine learning to identify variables that best classified children receiving therapy for language disorder among a sample of 483 3- to 5-year-old children (54%…
Descriptors: Language Impairments, Disability Identification, Clinical Diagnosis, Preschool Children

Woodward, John P.; Carnine, Douglas W. – Journal of Learning Disabilities, 1988
The article reviews Intelligent Computer Assisted Instruction (ICAI), an area of artificial intelligence and notes its shortcomings for learning disabled students. It is suggested that emphasis on antecedent knowledge (important facts, concepts, rules, and/or strategies for the content area) and content analysis and design techniques would make…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Educational Technology, Elementary Secondary Education