African Traditional Religion in the Age of Artificial Intelligence and Digital Culture: Continuity, Disruption, and the Negotiation of Sacred Knowledge

This paper examines what happens when African Traditional Religion (ATR) meets artificial intelligence (AI) and digital culture. Something significant is at stake in this encounter. Platforms, algorithms, and AI systems are changing how religious knowledge is shared, practised, and understood across the world. Yet ATR has received very little attention in this growing conversation. This study draws on Walter Mignolo’s decolonial thinking and his concept of ‘border thinking’ to make sense of what is happening. The approach provides a way of understanding how indigenous knowledge systems relate to the dominant frameworks that shape digital technology. Qualitative content analysis was used to examine digital platforms, practitioner testimonies, and recent scholarly works published between 2023 and 2026. Four themes emerged from the analysis. First, the digitisation of ATR sacred knowledge and the commercial pressures this creates. Second, the rise of AI-generated ritual content and what this means for authenticity. Third, how ATR communities are using digital platforms to build belonging and transmit tradition. Fourth, the deeper epistemological tensions that arise when a predominantly oral, embodied tradition is represented through textual and algorithmic media. The findings show a real tension at the heart of digital engagement with ATR. Access expands, but so do new forms of extraction and misrepresentation. AI tools are producing ATR-related content that practitioners find both occasionally helpful and deeply unsettling. The paper argues that ATR communities and scholars need to take the lead in shaping their digital presence. Community-controlled frameworks are needed to protect sacred knowledge without cutting off meaningful engagement with technology. This calls for digital sovereignty protocols, community-owned platforms, and AI literacy education that speaks directly to ATR contexts.