Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with challenges in social communication and interaction as well as stereotyped and repetitive behaviors, interests, and activities. Students with ASD often prefer to engage with technology because of its predictability and limited social demands. In recent years, the application of Artificial Intelligence (AI) in education has gained considerable attention. The present study aims to reveal the research trends regarding the design and development of AI teaching interventions in special education, especially for students with ASD, who often face significant challenges in academic, cognitive, and social domains. A search of the research literature from 2018 to 2024 in three electronic databases identified 1762 records. After applying eligibility criteria, 13 empirical studies were finally included, which were coded and analyzed in detail. The results demonstrated the potential of AI technology in supporting students with ASD in their learning, while also identifying gaps that warrant further investigation. This article concludes with future considerations for how AI could support students with ASD, emphasizing there are still gaps in the research, particularly in terms of long-term effectiveness and the standardization of methodologies for AI-based educational practices.

Background: Parenting a child with Autism Spectrum Disorder (ASD) may lead to emotional distress. However, it has been recognized that it can also be accompanied with positive experiences that may ...

The present study records and evaluates science-teaching interventions for students with intellectual disability (ID) or Autism Spectrum Disorder (ASD) at all education levels, for better access to the general curriculum. We conducted a systematic review of the primary research literature published from 2008 to 2018. A total of 24 single-case designs were identified through determined inclusion criteria. The articles were coded based on specific variables, and they were analyzed as a descriptive synthesis. A synthesis was then approached and the PND effect size was calculated. The study concludes that using systematic instruction with supplemental practices and technology support in teaching science to students with ID or ASD will allow them to make more meaningful natural-world connections through their learning. In addition, designing postsecondary programs for this population will contribute to building a society with equal participation and progress. The necessity for continuous research in teaching science to students with ID or ASD is also emphasized.

Exploring the individual differences of the longitudinal growth of early numeracy (EN) in young children with Intellectual disabilities (IDs) prerequires the critical stage of exploring and validating the potential factor structure. Despite the fact that Relational Skills (RS), Counting Skills (CS) and Operations (O) are expected to constitute distinct domains of EN, there is not sufficient evidence to support either the above position or the position that they are different means of assessing a general-informal numeracy skill construct. This study was designed to shed light in the field through the evaluation of a measurement model describing the structure of RS, CS and O domains of EN and their interrelation. The sample included N = 155 children with IDs, enrolled in special school classes, mentally aged between 5;02 (y;m) and 6;10 (M = 5.11, SD = 0.974). Confirmatory Factor Analysis indicated a “good fit” of the two-factor model (RS, CS + O) of EN in ID (RMSEA=.029 < 0.08, CFI = 0.98 ≥ 0.90 and SRMR = 0.000 < 0.08). No measurement invariance across gender was reported for the proposed two-factor model of EN. The nested EN models validated full measurement invariance across gender. Finally, educational implications are discussed.