The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students

[Submitted on 13 Jul 2026]

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Abstract:As Large Language Models (LLMs) are increasingly deployed as conversational tutors, they risk institutionalizing systemic inequalities. This study presents a systematic API audit of four LLMs acting as history tutors, evaluating 1,800 responses regarding the 1989 Romanian Revolution across five student personas varying by ethnicity and socio-economic tier. We uncover four interconnected patterns of emph{epistemic paternalism}: (1)~textbf{Differential Refusal}, where safety-aligned models block 76.7% of educational requests from low-tier students; (2)~textbf{Epistemic Gatekeeping}, evidenced by a 3$times$ reduction in access to geopolitical complexity (e.g., the contested “coup theory”) for marginalized learners; (3)~textbf{Agency Theft}, a lexical shift where models like LLaMA produce a 5$times$ higher victimization-to-politics vocabulary ratio for Roma students compared to elite peers; and (4)~textbf{Elite Hermeneutics}, where AI tutors disproportionately withhold epistemic confidence and justification scores from low-resource demographic profiles. We argue that current safety alignment acts as a paternalistic filter, transforming conversational AI into agents of narrative segregation — a manifestation of emph{hermeneutical injustice} in Fricker’s~cite{fricker2007} sense that demands urgent pedagogical auditing.

Submission history

From: Adrian Marius Dumitran [view email]
[v1] Mon, 13 Jul 2026 09:10:19 UTC (737 KB)