Exploring Artificial Intelligence in Inclusive Education: A Systematic Review of Empirical Studies
Jiahui Li, Yuyang Yan, Xiaojun Zeng
Abstract
This systematic review synthesizes empirical evidence on the role of Artificial Intelligence (AI) in inclusive education. The review aimed to examine (1) the impact of AI technologies on learning outcomes and engagement among students with special needs, (2) barriers and enablers influencing AI adoption by educators, and (3) the role of theoretical frameworks in guiding AI-based interventions. A comprehensive search was conducted in Scopus, Web of Science, DOAJ, and Google Scholar for English-language, peer-reviewed studies published between 2020 and 2025. Sixteen (16) studies met the inclusion criteria and were appraised using the Mixed Methods Appraisal Tool (MMAT). Findings indicate that AI tools enhance personalization, accessibility, and engagement, particularly for learners with disabilities, while barriers such as infrastructure gaps and low digital literacy persist. Enablers include institutional support and teacher training, though theoretical frameworks were inconsistently applied. Limitations include the exclusion of grey literature and reliance on short-term studies. AI can advance inclusive education when integrated with ethical, pedagogical, and institutional strategies, while future research should prioritize longitudinal, theory-driven, and culturally responsive models.