AI-driven language learning platforms are expanding rapidly, yet their underlying pedagogical and technological architectures remain poorly aligned with the linguistic, infrastructural, and epistemological realities of African language contexts. Despite increased adoption, African languages remain underrepresented in both training data and pedagogical design. Existing computer-assisted language learning (CALL) tools such as Duolingo, Memrise, and earlier platforms like Kiswahili kwa Kompyuta (KIKO) continue to rely predominantly on text-driven and decontextualized interaction, with limited support for sustained oral production and multimodal engagement. While many platforms incorporate audio and speech-based features, they typically prioritize standardized language forms and are optimized for technological conditions that do not consistently align with many African learning environments. Methodologically, my paper adopts a design-based research (DBR) approach, positioning the study as an initial design phase that combines comparative platform analysis with multimodal learning theory to generate context-sensitive design principles for African CALL. The paper represents the design articulation stage of DBR, focusing on problem framing and principle generation to guide future implementation and iterative testing. My analysis identifies three persistent limitations: the marginalization of dialectal variation, the constrained integration of interactive oral practice, and the isolation of language from its social and performative contexts. In response, I advance a design framework structured around four principles: audio-first and tone-aware interaction; integration of narrative and oral traditions; support for dialectal variation; and adaptability to low-resource environments. By shifting from critique to design, my paper offers a scalable model for developing CALL systems grounded in African language practices and knowledge systems.