- AI-augmented interventions show preliminary, mostly short-term improvements in social engagement, communication, emotion recognition and adaptive participation for autistic individuals.
- Methodological quality varied widely; only two studies met What Works Clearinghouse standards, many lacked experimental control, fidelity reporting, outcome measurement or adequate data.
- Most interventions involved human oversight, supporting parents, educators or clinicians; more rigorous, well designed research is needed to confirm effectiveness and guide implementation.
Autism. 2026 Jul 30:13623613261470855. doi: 10.1177/13623613261470855. Online ahead of print.
ABSTRACT
Artificial intelligence (AI) has been increasingly integrated into autism interventions to support personalization and scalability; however, the strength of empirical evidence supporting these approaches remains unclear. In this systematic review, we synthesized and critically appraised experimental studies evaluating AI-based interventions for autistic individuals with a specific focus on intervention outcomes and methodological rigor. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and a preregistered PROSPERO protocol, we searched databases and identified 13 eligible studies, including randomized controlled trials, quasi-experimental group designs, and single-case experimental designs. Reviewed studies targeted social engagement, communication, emotion recognition, empathy, adaptive participation, and symbolic play and predominantly employed human-in-the-loop models involving parents, educators, or clinicians. Methodological quality was evaluated using design-appropriate quality indicators and What Works Clearinghouse (WWC) standards. Although most studies reported positive short-term effects on participant-level outcomes, rigor varied considerably. Only two studies met WWC standards, five met standards with reservations, and six did not meet standards due to limitations related to experimental control, fidelity reporting, outcome measurement, or data adequacy. Overall, AI-based interventions show promise as tools to augment human-delivered autism interventions, but the current evidence base is preliminary. More rigorous research is needed to establish effectiveness and inform implementation in autism services.Lay AbstractArtificial intelligence, often called AI, is increasingly being used to support services for autistic children. AI tools can help adults such as parents, teachers, and therapists personalize instruction, track progress, and provide feedback during everyday activities. However, it is not yet clear how strong the research evidence is for AI-based interventions. In this review, we examined studies that tested AI-supported interventions designed to improve learning and behavior outcomes for autistic individuals. We carefully reviewed 13 studies and evaluated how well these studies were designed and conducted. The studies focused on areas such as social engagement, communication, emotion understanding, empathy, and participation in daily routines. Most interventions used AI to support, rather than replace, adult guidance. Although many studies reported positive short-term improvements, we found that many had important limitations in their research design. Only a small number of studies met strong standards for research quality. This means that more careful and well-designed studies are needed before AI-based interventions can be widely recommended. Overall, AI shows promise as a tool to support autism intervention, but stronger evidence is needed to understand when, how, and for whom these tools are most helpful.
PMID:42533285 | DOI:10.1177/13623613261470855
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