Theorising Algorithmic Colonialism: Generative AI and the Threat to Indigenous Knowledge Systems in African Higher Education

The rapid proliferation of generative artificial intelligence in higher education has precipitated a new crisis of legitimacy for Indigenous Knowledge Systems. While the utility of large language models is widely touted, this paper critically interrogates the epistemological architecture of these tools, theorising algorithmic colonialism as a curricular mechanism of epistemic erasure in post-#FeesMustFall South African universities. Designed as a conceptual paper, the article extends the boundaries of knowledge by theorising peer-reviewed literature and policy frameworks between 2020 and 2025 to understand the specific threat Generative AI poses to Indigenous Knowledge Systems within African curricula. The principal analytical findings reveal that Large Language Models rely on data extractivism and linguistic hegemony, mechanisms that systematically marginalise Indigenous Knowledge Systems in favour of Western cognitive models. Drawing on decoloniality and critical digital pedagogy, the paper argues that the uncritical integration of these tools into African university curricula risks entrenching a monoculture of the mind where machine-generated consensus displaces localised, context-specific wisdom. The analysis explores the specific mechanisms through which algorithmic bias reinforces historical inequities. Consequently, the paper proposes a robust, operational framework for AI sovereignty within African higher education grounded in data trusts, Reinforcement Learning from African Feedback (RLAF), and algorithmic literacy as counter-conduct. The paper concludes that for the Fourth Industrial Revolution to be emancipatory rather than recolonising, the curriculum must explicitly teach students to audit the colonial logic embedded within the code itself.