Disability services in higher education: Statistical disparities and the potential role of AI in bridging institutional gaps

TL;DR

This study finds significant disparities in disability services between two-year and four-year colleges and suggests AI could help bridge these gaps.

Contribution

The novel contribution is identifying statistical disparities in disability services and proposing AI as a potential solution to improve accessibility in higher education.

Findings

Two-year institutions have lower disability disclosure rates (15%) compared to four-year institutions (35%).

Accommodation provision is significantly lower at two-year institutions (9.47%) than at four-year institutions (28.40%).

Staff-to-student ratios are worse at two-year institutions (1:200) compared to four-year institutions (1:75).

Abstract

Disparities in disability services between two-year and four-year higher education institutions pose challenges to achieving equitable access to accommodations. This study applies a robust quantitative analysis of the National Center for Education Statistics (NCES) dataset, utilizing multiple regression models and exploratory factor analysis to identify institutional characteristics that impact disability service quality. Results reveal statistically significant differences in disability disclosure rates (15% at two-year institutions compared to 35% at four-year institutions, t(68) = -11.50, p < 0.001, Cohen’s d = 2.25), accommodation provision (9.47% versus 28.40%, t(68) = -18.01, p < 0.001, Cohen’s d = 3.10), and staff-to-student ratios (1:200 versus 1:75, r = 0.65, p < 0.01). This study also explores the potential role of artificial intelligence (AI) in mitigating disparities by improving access to accommodations through adaptive learning platforms, real-time captioning, and automated awareness campaigns. While AI adoption was not directly analyzed, existing literature suggests that AI-driven interventions have the potential to improve disclosure rates, enhance service delivery, and reduce administrative burdens. The findings provide a data-driven foundation for policy recommendations, emphasizing targeted funding, AI-enabled accessibility initiatives, and faculty training to foster more inclusive learning environments.

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