Generative AI in disability-inclusive learning: a bibliometric and systematic literature analysis

Generative Artificial Intelligence (GenAI) has rapidly permeated education, with growing implications for disability-inclusive practice. Objective: This review maps GenAI uses for students with disabilities since public LLM adoption, identifies research clusters, and surfaces gaps. Methods: Following SPAR-4-SLR, we searched Scopus and Google Scholar (publications Jan 1, 2022–Feb 6, 2025; English; journals/conferences). After screening, 88 records were retained for the qualitative SLR; a relevance subset (n = 49, score = 3) underwent bibliometric and text-mining analysis using TF-IDF, K-Means (k = 5), and PCA based visualisation; keyword co-occurrence networks were built in VOSviewer. Results: Five clusters emerged: (1) adaptive tools for autism and language learning; (2) inclusive/early-childhood AI integration; (3) game-based/adaptive learning; (4) broad ChatGPT applications across K–12/higher/special education; and (5) frameworks/conceptual models. ASD and dyslexia dominate; visual/hearing/motor impairments are underrepresented. Conclusions: GenAI shows promise for personalisation, AAC, and teacher support, but evidence is early-stage and uneven across disabilities. Recommendations include standardised reporting (datasets, prompts, guardrails), longitudinal evaluation, and policy frameworks aligning Universal Design for Learning (UDL) with AI ethics and privacy. A replication package (data/code) and a taxonomy for GenAI-inclusive learning are proposed.