Despite rapid advancements in multimodal large language models (MLLMs), their ability to process low-resource African languages in document-based visual question answering (VQA) tasks remains limited. This paper evaluates three state-of-the-art MLLMs—GPT-4o, Claude-3.5 Haiku, and Gemini-1.5 Pro—on WAEC/NECO standardized exam questions in Yoruba, Igbo, and Hausa. We curate a dataset of multiple-choice questions from exam images and compare model accuracies across two prompting strategies: (1) using English prompts for African language questions, and (2) using native-language prompts. While GPT-4o achieves over 90% accuracy for English, performance drops below 40% for African languages, highlighting severe data imbalance in model training. Notably, native-language prompting improves accuracy for most models, yet no system approaches human-level performance, which reaches over 50% in Yoruba, Igbo, and Hausa. These findings emphasize the need for diverse training data, fine-tuning, and dedicated benchmarks that address the linguistic intricacies of African languages in multimodal tasks, paving the way for more equitable and effective AI systems in education.

Code-switching (CS) presents a significant challenge for Automatic Speech Recognition (ASR) systems, particularly in low-resource settings. While multilingual ASR models like OpenAI Whisper Large v3 are designed to handle multiple languages, their high computational demands make them less practical for real-world deployment in resource-constrained environments. In this study, we investigate the effectiveness of fine-tuning both monolingual and multilingual ASR models for Yoruba-English CS speech. Our results show that unadapted monolingual ASR models outperform Whisper Large v3 in a zero-shot setting on CS speech. Fine-tuning significantly reduces WER for both monolingual and multilingual models, with monolingual models achieving over a 20% WER reduction on CS and Yoruba speech while maintaining lower computational costs. However, we observe a trade-off, as fine-tuning leads to some degradation in English recognition, particularly for multilingual models. Our findings highlight that while multilingual models benefit from fine-tuning, monolingual models provide a computationally efficient and competitive alternative for CS-ASR, making them a viable choice for resource-constrained environments.