From Tacit Knowledge Distillation to AI-Enabled Culture Revitalization: Modeling Knowledge Cycles in Indigenous Cultural Systems

Abstract

This study addresses the challenge of digitally modeling Indigenous Traditional Ecological Knowledge (TEK) in a manner that respects and preserves its epistemic integrity. Grounded in ethnographic inquiry and system design, the research introduces a four-tier knowledge typology that conceptualizes how tacit, explicit, tribal and cultural knowledge circulate within Indigenous communities. This cyclical model highlights recursive and embodied processes of knowledge internalization, transmission, and integration, offering a dynamic alternative to linear knowledge flow frameworks. Building upon this epistemological foundation, this study traces the transition from traditional data practices, which are centered on oral histories, ritual performances, and ecological observation, to a contemporary AI-assisted architecture that operationalizes these forms through structured semantic enrichment, modular knowledge storage, and culturally aligned reasoning systems. The proposed system integrates layered components, from data acquisition to multi-agent inference models, while embedding ethical protocols that affirm community sovereignty and relational authority. The findings suggest that TEK systems can be effectively encoded into modern digital infrastructures without erasing their socio-cultural contexts. By foregrounding Indigenous epistemologies within system design, the research advances a critical paradigm for culturally responsive knowledge technologies in sustainability, education, and heritage preservation.