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Reasonable adjustments for neurodivergent students in higher education: generative AI, accessibility, and mediated authorship
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2026-05-11
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Abstract
Neurodivergent students face significant disadvantages in higher education, where assessment often privileges neuro-normative written expression. This article argues that Large Language Models (LLMs) should be framed as accessibility tools that scaffold expression while preserving the student’s intellectual authorship, reframing the debate from academic integrity to equity. The argument is grounded in UK equality law and contends that the Court of Appeal’s decision in University of Bristol v Abrahart requires universities to distinguish competence standards from the methods used to assess them. On this basis, the regulated use of LLMs may amount to a reasonable adjustment where a student’s barrier lies in written expression rather than reasoning. The article advances a four-pillar model for responsible integration, addressing risks such as algorithmic bias, opacity, and data privacy. It concludes that LLMs, situated within traditions of mediated authorship, can support inclusion without displacing academic standards.
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Morgan, D. (2026) Reasonable adjustments for neurodivergent students in higher education: generative AI, accessibility, and mediated authorship, Disability & Society, DOI: 10.1080/09687599.2026.2667528
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Journal article
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en
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© 2026 The Author. Published by Routledge (Taylor & Francis). This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1080/09687599.2026.2667528
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0968-7599