Effectiveness of intervention using artificial intelligence for students with disabilities: systematic review and meta-analysis
Abstract
BackgroundAlgorithmic systems do not sufficiently capture the contextual nuances of disability, thereby risking model bias and reducing ecological validity. Hence, this review aims to investigate the various pedagogical and ethical concerns that have resulted from the integration of artificial intelligence (AI) into special education.MethodsWe conducted a systematic review and meta-analysis to investigate the effects of intervention using AI on students with disabilities. For this purpose, 16 papers were selected from an electronic database, and relevant data were extracted.ResultsOur analysis revealed that the primary targets of AI intervention were students with learning disabilities (31.3%). Regarding methodological characteristics, only one study was a randomized clinical trial. The overall effect size (ES) was significantly large (Hedges'g = 1.263). The ESs of 14 studies were significant (87.5%), whereas those of two studies were insignificant (12.5%). Subgroup analysis revealed significant differences in ESs based on outcome, type of disability, grade, and implementer. Within each subgroup, the largest ESs were observed in the cognitive outcome, ADHD, and university student groups, as well as when the implementer was a researcher.ConclusionsThe results of this study suggest that AI interventions for students with disabilities are effective and that their effectiveness is increased when used as collaborative support tools, rather than replacing teachers in educational settings.
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BibTeX
@article{park2026effectiveness,
title = {{Effectiveness of intervention using artificial intelligence for students with disabilities: systematic review and meta-analysis}},
author = {Eun-Young Park and Ok-In Lee and Heeju Kim and Hun-Soo Bok and Soojung Chae},
journal = {Frontiers in Public Health},
year = {2026},
month = nov,
doi = {10.3389/fpubh.2026.1936957},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1936957},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Effectiveness of intervention using artificial intelligence for students with disabilities: systematic review and meta-analysis
AU - Eun-Young Park
AU - Ok-In Lee
AU - Heeju Kim
AU - Hun-Soo Bok
AU - Soojung Chae
PY - 2026
DA - 2026/11/06
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1936957
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1936957
AB - BackgroundAlgorithmic systems do not sufficiently capture the contextual nuances of disability, thereby risking model bias and reducing ecological validity. Hence, this review aims to investigate the various pedagogical and ethical concerns that have resulted from the integration of artificial intelligence (AI) into special education.MethodsWe conducted a systematic review and meta-analysis to investigate the effects of intervention using AI on students with disabilities. For this purpose, 16 papers were selected from an electronic database, and relevant data were extracted.ResultsOur analysis revealed that the primary targets of AI intervention were students with learning disabilities (31.3%). Regarding methodological characteristics, only one study was a randomized clinical trial. The overall effect size (ES) was significantly large (Hedges'g = 1.263). The ESs of 14 studies were significant (87.5%), whereas those of two studies were insignificant (12.5%). Subgroup analysis revealed significant differences in ESs based on outcome, type of disability, grade, and implementer. Within each subgroup, the largest ESs were observed in the cognitive outcome, ADHD, and university student groups, as well as when the implementer was a researcher.ConclusionsThe results of this study suggest that AI interventions for students with disabilities are effective and that their effectiveness is increased when used as collaborative support tools, rather than replacing teachers in educational settings.
SN - 2296-2565
ER -