Generative artificial intelligence and the marginalization of minoritized knowledges in higher education: the case of disability
For scholars in critical technology studies and disability studies, this work highlights how AI systems perpetuate epistemic injustice, but it is primarily a theoretical critique without empirical results.
The paper argues that generative AI in higher education marginalizes non-hegemonic knowledges, using disability as a case study to show how Anglophone, Western-centric datasets reinforce epistemic coloniality and double marginalization of persons with disabilities.
Generative artificial intelligence redefines higher education by restructuring the processes through which scientific knowledge is produced and validated. These systems are not neutral; they actively contribute to the marginalization of non-hegemonic epistemologies. This research draws upon educational sciences, critical technology studies, and disability studies to demonstrate that training datasets, which remain predominantly Anglophone and Western-centric, reinforce epistemic coloniality. The situation of persons with disabilities provides a particularly clear illustration of this phenomenon. Technological architectures frequently confine these individuals to reductive stereotypes or exclude them from the design process, leading to a double marginalization. This article examines whether a hybridization between the researcher and the machine might preserve epistemic plurality, while acknowledging the structural limitations inherent in algorithmic correction when used as a purely palliative strategy.