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Educational inequality continues to impede progress toward Sustainable Development Goal 4 in Sub-Saharan Africa, where teacher shortages, overcrowded classrooms, and high data costs exclude many learners. High-quality human tutoring can deliver substantial learning gains, but its financial and logistical demands make broad deployment difficult. This comparative synthesis draws on randomized trials, large field pilots, case studies, and policy and monitoring reports to compare human tutoring, AI-supported tutoring, and low-bandwidth mobile-first interventions through economic, technological, inclusion, and decolonial perspectives. Evidence from Ghana, Sierra Leone, and Kenya indicates that mobile-first systems delivered through channels such as WhatsApp and SMS can approximate tutoring-like gains while lowering marginal cost and data use, supporting overstretched teachers and underserved learners. Their value remains constrained by infrastructure, teacher capacity, data governance, and risks of externally imposed AI systems. The study concludes that AI tutors are most defensible when embedded in teacher-led practice, designed for low-bandwidth environments, governed around local curricula, languages, and communities, and accompanied by investment in infrastructure and professional development.