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

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Disability Studies

Background:

• Generative Artificial Intelligence (GenAI) is increasingly used in education.
• Implications for disability-inclusive practices are significant but underexplored.
• The rapid adoption of large language models (LLMs) necessitates a review of GenAI applications for students with disabilities.

Purpose of the Study:

• To map the uses of GenAI for students with disabilities since the public adoption of LLMs.
• To identify key research clusters and trends in this emerging field.
• To highlight existing research gaps and areas for future investigation.

Main Methods:

• Systematic Literature Review (SLR) following SPAR-4-SLR guidelines.
• Searches conducted on Scopus and Google Scholar (Jan 2022 - Feb 2025).
• Bibliometric and text-mining analysis (TF-IDF, K-Means, PCA, VOSviewer) on 49 relevant records.

Main Results:

• Five research clusters identified: adaptive tools for autism/language, inclusive AI integration, game-based learning, broad ChatGPT applications, and conceptual frameworks.
• Autism Spectrum Disorder (ASD) and dyslexia are dominant research areas.
• Visual, hearing, and motor impairments are underrepresented in current research.

Conclusions:

• GenAI shows potential for personalized learning, Augmentative and Alternative Communication (AAC), and teacher support for students with disabilities.
• Current evidence is preliminary and varies across different disability types.
• Recommendations include standardized reporting, longitudinal evaluations, and policy frameworks integrating Universal Design for Learning (UDL) with AI ethics and privacy.