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Paper short abstract

Kenya’s multilingual education faces a widening gap between policy and practice as AI tools rarely support Indigenous languages. Limited digital access reinforces English dominance. Inclusive, community-driven AI and stronger Indigenous data governance are needed to support multilingual learning.

Paper long abstract

Kenya’s multilingual education system is increasingly shaped by AI-mediated communication, where translation technologies offer the potential for broader language access. However, the gap between language-in-education policies and actual classroom practice has widened due to inadequate digital infrastructure and the limited inclusion of African Indigenous languages in mainstream AI systems. These shortcomings contribute to renewed linguistic marginalisation despite national commitments to multilingualism. This study investigates how Competency-Based Curriculum (CBC) language policies function in digitally influenced learning environments, identifies infrastructural constraints affecting multilingual instruction, and explores how AI translation tools might be redesigned to better align with local language priorities. A mixed-methods design was used, combining surveys and semi-structured interviews with educators (n=120), students (n=100), policymakers (n=30), and community leaders (n=50) in Kisii and Homabay counties. The data examined experiences with policy implementation, access to digital and AI resources, and user perceptions of translation technologies. Quantitative analysis assessed relationships between language use and learning outcomes, while qualitative perspectives revealed systemic challenges and unequal technological access. Guided by Vygotsky’s sociocultural theory, the study highlights the role of language in shaping learning processes. Critical language policy analysis draws attention to power structures influencing language technologies, and decolonial computing brings Indigenous data sovereignty to the forefront. Findings indicate persistent gaps between multilingual policy goals and classroom realities, alongside minimal digital support for Indigenous languages. Current AI tools largely reinforce English dominance, yet community-led language documentation offers promising alternatives. The study recommends strengthening Indigenous data governance and investing in culturally grounded AI systems.