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Against Prohibition: Generative AI, Disabled Students, and the Case for Critically Governed Inclusion in Higher Education

Primary research

#1112

T1new
Topic
unassigned (set during synthesis)
First seen
2026-08-01 07:16:00
Last seen
2026-08-01 07:16:00

Source raw items (1)

  • Semantic Scholar2026-08-01 07:15:27
    Against Prohibition: Generative AI, Disabled Students, and the Case for Critically Governed Inclusion in Higher Education

    Since the public release of large language models in late 2022, universities have faced pressure to prohibit or severely restrict student use of generative artificial intelligence. This paper argues that outright bans are the wrong response, and that they are wrong in a particular way: they redistribute harm towards disabled students while doing little to address the problems they claim to solve. Using conceptual analysis grounded in the social model of disability, the Capability Approach, and the author’s Critical Disability Framework for AI in Education (CDF-AIED), the paper develops three claims. First, prohibition is a category error that treats a general-purpose cognitive technology as if it were a discrete cheating device, and its enforcement mechanisms, particularly AI-detection software, produce discriminatory false positives. Second, the documented biases of AI systems, which this author has examined elsewhere, justify governance rather than exclusion, since a banned technology cannot be audited, contested, or redesigned from within the institution. Third, generative AI offers a distinctive set of affordances for disabled students, including executive function scaffolding, format transformation, communication support, and reduced dependence on formal disclosure, that no previous assistive technology has combined in one tool. The discussion sketches a future in which AI functions as a governed support infrastructure and specifies the institutional conditions, from co-design to disaggregated evaluation, under which that future becomes defensible rather than naive. The paper concludes that the biases of AI are an argument for institutional presence and power over these systems, not for surrendering both through prohibition.