Technical brief

Using Artificial Intelligence (AI) to improve math accessibility for students with visual impairments

June 2022

By: Kang Xue, Elizabeth Barker


Description

This study, which is part of a larger project that aims to make online math more accessible to students with visual impairments (VI), examines the text quality of math assessment items for students with VI who use screen readers. Using data from about 29.5 million students taking standard versions of the MAP Growth math assessment, and 48,845 students taking accessible versions, we identified high-quality items, those that measured achievement for both students with and without VI equally well, and low-quality items, which showed differences between the two groups of students. The researchers introduced three word embedding methods and three classifiers to predict item quality for accessible assessments. This work advanced our understanding of barriers, and used cutting-edge technologies to develop a new way to better present math content online to improve accessibility and increase the opportunity to learn math for students for students with VI.

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