Generative AI and the marginalization of minoritized knowledges in higher education: the case of disability

Fatiha Tali-Otmani (2026) — EFTS, Grhapes. arXiv preprint.

Overview

This paper argues that generative-ai systems in higher-ed are not epistemically neutral — they actively marginalize non-hegemonic ways of knowing. Drawing on educational sciences, critical technology studies, and disability studies, Tali-Otmani demonstrates how predominantly Anglophone and Western-centric training data reinforces epistemic coloniality. The situation of persons with disabilities provides a particularly clear illustration, where technological architectures confine them to reductive stereotypes or exclude them from the design process entirely.

Double Marginalization

The paper identifies a double marginalization for disabled learners: first, their epistemologies are underrepresented in AI training data; second, they are excluded from the design process that shapes AI tools used in education. This compounds existing equity challenges documented in digital-literacy-illusion research, where surface-level AI proficiency masks deeper exclusion. The concept extends inclusive-ai frameworks by focusing on knowledge production rather than just access.

Epistemic Coloniality

Training data predominantly sourced from Anglophone, Western academic traditions means that generative-ai outputs reflect and reinforce those epistemic frameworks. When deployed in higher education without critical awareness, these systems can present non-Western or disability-centered knowledge as less legitimate. This connects to concerns in genai-assessment-governance about whose standards govern AI use in educational settings.

Proposed Hybridization

Tali-Otmani explores whether a researcher-machine hybridization could preserve epistemic plurality. Rather than rejecting AI tools outright, she examines whether collaborative human-AI processes might surface marginalized perspectives. However, she warns against treating bias-mitigation through algorithmic correction as a purely palliative strategy — structural limitations persist when the underlying training data and design processes remain unchanged.

Implications for AIED

For the special-education AIED community, this paper raises the stakes beyond accessibility to knowledge legitimacy. AI tools for disabled learners must not only be accessible but must also amplify rather than suppress their epistemological contributions. This requires rethinking ai-literacy to include critical awareness of whose knowledge is being represented and validated.

agentic-literacy-debt — 4 of 8 papers in May 28 scan

generative-ai — the technology whose epistemic effects are under scrutiny

special-education — the stakeholder context for disability-centered AI

higher-ed — the institutional setting where epistemic marginalization occurs

equity — broader concerns about fairness in AI education

bias-mitigation — technical approaches critiqued as insufficiently structural

inclusive-ai — design frameworks that this work challenges to go deeper

digital-literacy-illusion — surface competency masking deeper exclusion

genai-assessment-governance — parallel governance concerns about whose standards apply

Citation