Toward Equitable STEM Education: Low-Resource Machine Translation for Swahili-Speaking Muslim Communities

We present a Track 3 machine learning competition for the 6th MusiML Workshop 2026 focused on improving access to STEM education for Muslim communities in Swahili - speaking regions, particularly Zanzibar and coastal East Africa. The competition directly addresses educational inequities faced by Muslim learners, where language barriers intersect with a lack of culturally and religiously adapted STEM content. The challenge asks participants to develop low-resource machine translation systems that translate STEM educational material from English into Swahili while preserving scientific accuracy, mathematical correctness, educational clarity, and respect for Islamic cultural and ethical values. Participants will develop multilingual NLP and deep learning systems using ethically sourced educational datasets, multilingual corpora and STEM instructional materials that have been reviewed in consultation with local Muslim educators and community stakeholders. The competition will provide standardized datasets, public baselines, evaluation protocols and leaderboard-based benchmarking to encourage reproducible research in low-resource educational NLP. Systems will be evaluated using automatic ma- chine translation metrics together with human assessments that include educational quality, cultural appropriateness, and sensitivity to Islamic religious contexts. The proposed challenge promotes fairness, responsible AI, culturally aware NLP re- search and socially impactful machine learning that directly serves underserved Muslim communities.