Exploring the Affordances of Generative AI in Academic Writing for Disabled Student

Abstract

This study explores the use and attitudes towards generative AI technology among disabled students in higher education, addressing a gap in existing research on accessibility and inclusivity challenges for marginalized groups. Informed by a prior study and affordance theory, we surveyed 124 students with various disabilities (e.g., neurodiversity, dyslexia and social/communication impairment) about their use of and attitudes toward generative AI during academic writing. We identified three key affordances provided by generative AI—explainability, expressibility, and plannability—that positively affect disabled students' writing processes. However, our study also highlights significant areas where generative AI remains insufficient in addressing barriers faced by disabled students, such as concerns about hallucination, loss own voices, and academic integrity. Our findings offer practical implications for both developers and educational practitioners. These include the need to design more inclusive generative AI technologies and to promote AI literacy, along with providing guidance and training for both students and staff in higher education institutions.

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