Enhancing Accessibility Testing Using Machine Learning: A Systematic Mapping Study

Abstract

Ensuring accessibility in digital systems is essential for inclusive software design. Traditional accessibility testing methods are often manual, time-consuming, and lack scalability. In response, machine learning (ML) techniques have emerged as a promising avenue for automating and enhancing accessibility evaluations. This systematic mapping study explores how ML has been applied in accessibility testing, categorizing approaches by algorithm type, target accessibility issues, platform focus, and validation strategies. The study synthesizes prevailing trends, highlights existing gaps, and outlines future research directions. This work aims to provide a foundational reference for researchers and practitioners aiming to advance ML-driven accessibility testing.