Against Prohibition: Generative AI, Disabled Students, and the Case for Critically Governed Inclusion in Higher Education
DOI:
https://doi.org/10.61424/issej.v4i3.963Keywords:
Generative artificial intelligence, disability, higher education, assistive technology, academic integrity, Capability ApproachAbstract
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.
References
Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160–1173.
Benjamin, R. (2019). Race after technology: Abolitionist tools for the New Jim Code. Polity Press.
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, Article 38.
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Eccles, S., Hutchings, M., Hunt, C., & Heaslip, V. (2018). Disclosure of ‘disability’ in higher education: A qualitative study of student perceptions. Widening Participation and Lifelong Learning, 20(4), 191–208. https://doi.org/10.5456/WPLL.20.4.191
Equality Act 2010, c. 15 (UK). https://www.legislation.gov.uk/ukpga/2010/15
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police and punish the poor. St. Martin’s Press.
Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.
Goode, J. (2007). ‘Managing’ disability: Early experiences of university students with disabilities. Disability & Society, 22(1), 35–48. https://doi.org/10.1080/09687590601056204
Goodley, D. (2014). Dis/ability studies: Theorising disablism and ableism. Routledge.
Hamraie, A., & Fritsch, K. (2019). Crip technoscience manifesto. Catalyst: Feminism, Theory, Technoscience, 5(1), 1–34. https://doi.org/10.28968/cftt.v5i1.29607
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8(4), 49–62. https://doi.org/10.1177/160940690900800406
Kafer, A. (2013). Feminist, queer, crip. Indiana University Press.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779
Madriaga, M. (2007). Enduring disablism: Students with dyslexia and their pathways into UK higher education and beyond. Disability & Society, 22(4), 399–412. https://doi.org/10.1080/09687590701337942
McNicholl, A., Casey, H., Desmond, D., & Gallagher, P. (2021). The impact of assistive technology use for students with disabilities in higher education: A systematic review. Disability and Rehabilitation: Assistive Technology, 16(2), 130–143.
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
Nussbaum, M. C. (2011). Creating capabilities: The human development approach. Harvard University Press.
Oliver, M. (1990). The politics of disablement. Macmillan.
Russell Group. (2023). Russell Group principles on the use of generative AI tools in education. Russell Group.
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 342–363.
Seale, J. (2014). E-learning and disability in higher education: Accessibility research and practice (2nd ed.). Routledge.
Selwyn, N. (2022). The future of AI and education: Some cautionary notes. European Journal of Education, 57, 620–631. https://doi.org/10.1111/ejed.12532
Sen, A. (1999). Development as freedom. Oxford University Press.
Shakespeare, T. (2014). Disability rights and wrongs revisited (2nd ed.). Routledge.
Shepherd, N. J. (2025). Disability, data, and design: Toward a critical framework for evaluating AI in inclusive education. IRE Journals, 8(12), 2218–2230. https://doi.org/10.64388/IREV8I12-1719101
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), 31–40.
Terzi, L. (2005). Beyond the dilemma of difference: The capability approach to disability and special educational needs. Journal of Philosophy of Education, 39(3), 443–459. https://doi.org/10.1111/j.1467-9752.2005.00447.x
Thomas, C. (2007). Sociologies of disability and illness: Contested ideas in disability studies and medical sociology. Palgrave Macmillan.
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.
Whittaker, M., Alper, M., Bennett, C. L., Hendren, S., Kaziunas, L., Mills, M., Morris, M. R., Rankin, J., Rogers, E., Salas, M., & West, S. M. (2019). Disability, bias, and AI. AI Now Institute.
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Nicki James Shepherd
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
