A systematic review of the effects of AI on the educational performance of children with neurodevelopmental disabilities

Purpose This paper aims to provide a systematic research overview of the effects of artificial intelligence (AI) on the educational performance of children with neurodevelopmental disabilities (NDD) following the preferred reporting items for systematic reviews and meta-analyses statement. Design/methodology/approach The selected studies, published between November 2021 and November 2024, were identified through the search of SCOPUS, EBSCO, PsycInfo, ProQuest and two preprint-servers related to education. The selection criteria were based on (1) articles focusing on children and youth (ages 5–17 years), (2) articles focusing on children with NDD, (3) articles addressing student achievement, (4) articles reporting on studies that collected primary data and (5) articles reporting on studies that used school/researcher-administered assessments (objective) or self/hetero-reported measures (subjective). The screening of titles, abstracts and keywords left a final sample of n = 15 scientific papers. Findings The studies highlight outcomes classified into four primary categories: cognitive processing, academic performance, engagement and motivation and social and emotional learning. The most important findings are concerned with the difficulties faced by children in attention, memory, logical reasoning, reading, tasks related to science, technology, engineering, mathematics and in some cases engagement and motivation. Practical implications Empirically, the study outlines pathways for future research on AI in special education (SE). In practice, the study presents challenges in implementing AI in SE and discusses practical and policy implications. Originality/value To summarize, studies (e.g. Drigas and Ioannidou, 2013; Hopcan et al., 2023; Yang et al., 2024) provide valuable insights into AI's transformative potential in SE, however, most studies are limited to specific disabilities, technologies or demographic contexts, often neglecting a broader synthesis of AI's impact on NDD.