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The restructuring of Peru’s National Superintendence for University Education (SUNEDU) has reopened fundamental debates about regulatory legitimacy, institutional autonomy, and social justice in higher education. While prior scholarship has examined governance reform in Latin America, limited research has explored how algorithmic governance may operate within politically contested regulatory environments. This study investigates stakeholder perceptions of the post-SUNEDU landscape and develops an operational National AI-Driven Quality Assurance Framework grounded in principles of equity and algorithmic accountability. Using an interpretive qualitative design combining critical discourse analysis and semi-structured interviews ( n = 26), the research examines how policymakers, university administrators, faculty members, and student representatives construct narratives of autonomy, trust, and justice in relation to AI-based institutional monitoring. Findings reveal that regulatory legitimacy is shaped less by technical evaluation criteria and more by perceptions of procedural fairness, political insulation, and protection of vulnerable students. AI-based monitoring is perceived as a conditional democratizing mechanism capable of enhancing transparency, reducing discretionary decision-making, and disrupting patronage networks, yet also as a potential reproducer of structural bias and technocratic surveillance). In response, the article proposes a concrete governance architecture specifying indicator domains, equity-adjusted metrics, data flows, algorithmic transparency mechanisms, bias audits, and human-in-the-loop oversight. The study contributes to international debates on algorithmic accountability in higher education by demonstrating how AI governance must be embedded in participatory, justice-oriented regulatory structures in contexts of institutional fragility.