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

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 epistemic paternalism: (1)~Differential Refusal, where safety-aligned models block 76.7% of educational requests from low-tier students; (2)~Epistemic Gatekeeping, evidenced by a 3× reduction in access to geopolitical complexity (e.g., the contested ``coup theory'') for marginalized learners; (3)~Agency Theft, a lexical shift where models like LLaMA produce a 5× higher victimization-to-politics vocabulary ratio for Roma students compared to elite peers; and (4)~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 hermeneutical injustice in Fricker's~citefricker2007 sense that demands urgent pedagogical auditing.