Evaluation of an AI translator for adapting curriculum content to indigenous languages in primary schools.

Abstract

This study critically evaluates the potential of Artificial Intelligence (AI) translators for adapting curricular content to indigenous languages in primary education. Using a mixed-methods approach (quantitative and qualitative) that included technical evaluation of AI models and perspectives from specialists, teachers, and indigenous communities, the findings reveal a fundamental paradox. Although specialized AI systems show technical improvements in fluency and syntactic accuracy, they exhibit a structural insufficiency in cultural-pedagogical relevance, the core of meaningful adaptation. The most serious errors were related to cultural correspondence and linguistic calques. The research identifies risks such as "hidden standardization" (amplification of majority dialectal variants) and underscores that ethical governance and community sovereignty over data and processes are non-negotiable prerequisites for any implementation. It is concluded that AI cannot replace the creative and situated role of the human translator, but could function as a technical assistant within a "Community-Supervised Hybrid Model", where technology is subordinate to indigenous agency and control. Feasibility depends less on technical advancement and more on frameworks of epistemic justice and digital decoloniality.