The movement to include African languages in AI systems has concentrated on the continent’s largest languages, while the vast majority of Africa’s 2,000+ languages remain extremely low-resourced. For speakers of small, locally confined languages in ‘francophone’ multilingual West Africa, this creates a triple marginalisation: they are excluded first by the global dominance of English in AI, with user experiences in French being already degraded, and further by the emergent hierarchy among African languages themselves. Wolof and Pulaar are gaining AI visibility, but these are already languages of wider communication for rural multilinguals whose multiple primary languages — Baïnounk Gubëeher, Joola Kujireray, Balant Ganja, etc. — will foreseeably never have sufficient digital data to train dedicated language models. Yet these same speakers, routinely using five or more languages in their daily lives, possess multilingual competence potentially able to overcome monolingual AI architectures.
LILIEMA (Language-Independent Literacies for Inclusive Education in Multilingual Areas), a Senegalese association and literacy programme developed collaboratively since 2017, offers a promising site for exploring how existing AI tools can serve users at the margins of the digital language divide. Rather than selecting a single language, LILIEMA teaches letter-sound associations using the official alphabet for Senegalese languages across learners’ entire linguistic repertoires. Through this, language-independent AI tools do not need to “know” any specific small language to be useful; they can operate at the level of orthographic pattern, phonological structure, and pedagogical template creation.
We report on LILIEMA workshops conducted in May 2026 piloting the use of generative AI to create adaptive multilingual learning materials. We explore several concrete applications: (1) transliteration between widespread informal French-based lead-language writing practices and the official orthographies of Senegalese languages, enabling LILIEMA to bridge everyday written practice with standardised literacy; (2) generation of phonologically targeted word lists containing specific letters and letter combinations matching the sequential introduction order in LILIEMA’s beginner curriculum, drawn from any language in the learner’s repertoire; (3) creation of customisable exercise templates that teachers can adapt to specific language ecologies without requiring technical expertise; (4) production of varied exercise types at calibrated difficulty levels; (5) adaption of a core set of learning materials to each village’s specific multilingual profile and thus reducing the manual labour of customisation; (6) prompt engineering as a transferable literacy skill in its own right. Formulating effective prompts for AI tools through collective oral multilingual practice is itself a form of digital literacy and positions users as active agents in the AI ecosystem.
While we present on the development of sustainable AI infrastructures, we also address axes 1 and 3. We conduct ethnographic research on LILIEMA members and our own digital and AI practices transforming methodological frameworks, and through this advance epistemological considerations centring African language users, who are not mono- or bilingual but versatile multilinguals with longstanding experiences of sustaining oral multilingual practices alongside writing practices centred on focal languages of literacy. We conclude with recommendations for adapting AI tools to similar conditions of extreme digital language scarcity in thriving offline multilingual environments.