The Use of Generative AI by Students with Disabilities in UK Higher Education: A Mixed-Methods Survey of 125 Students

The Use of Generative AI by Students with Disabilities in UK Higher Education: A Mixed-Methods Survey of 125 Students - by Nicki James Shepherd

Abstract

Higher education's response to generative artificial intelligence (AI) has moved quickly from curiosity to policy, yet questions of who benefits from these tools and who is excluded remain under-examined. Students with disabilities - a population long under-represented in higher education and demonstrably reliant on assistive technologies - may have much to gain from generative AI, but also face specific risks from it; empirical evidence on their actual practices is scarce. This study asks how students with disabilities at a UK university use generative AI in academic writing, what concerns they hold about it, and what institutional support they want. A mixed-methods online survey, anchored in a five-element generative AI literacy framework, was distributed to all 1,797 students registered as disabled at one UK university during February and March 2024. The survey returned 125 valid responses, a 7.0 % response rate. Closed items were analysed descriptively; open-text responses were coded inductively through content analysis. Seventy-eight percent of respondents reported using generative AI in their studies, with ChatGPT (53 %) the dominant tool, supplemented by rewriting applications such as Grammarly (10 %) and translation services. Respondents used these tools across the full writing pipeline - explaining concepts, structuring arguments, summarising readings, and refining prose. Concerns clustered around the accuracy of AI outputs, threats to academic integrity, and the inequity introduced by paid subscriptions. Participants voiced strong demand for involvement in institutional AI policy (M = 4.03 on a five-point scale) and for university-provided training (M = 3.95). The paper closes with policy and pedagogical recommendations for building a more inclusive institutional response to generative AI.

Journal Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)
Published 25 December 2025
Volume 4(4)
Pages 142-151
ISSN 2959-6386
DOI 10.60087/jklst.vol4.n4.016

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