Daniel Morgan
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.Points of interestThis article highlights how university assessments often focus on perfect writing, which can create unfair barriers for students with dyslexia, autism, or ADHD.It argues that Artificial Intelligence (AI) writing tools, including Large Language Models (LLMs), should be viewed as accessibility aids that help students organise their thoughts and express their ideas clearly.It introduces the idea of ‘mediated authorship’ to explain that AI support can be compatible with academic integrity. The student provides the ideas and arguments, while the tool helps improve clarity and structure.It argues that, under UK equality law, permitting regulated use of AI tools may be a ‘reasonable adjustment’ for some disabled students.It concludes that this approach helps ensure students are assessed on their knowledge and critical thinking, rather than on their ability to match a narrow style of academic writing.