Despite serving over 100 million speakers as a vital African lingua franca, Swahili remains critically under-resourced for Natural Language Processing, hindering technological progress across East Africa. We present a scalable solution: a controllable synthetic data generation pipeline that produces culturally grounded Swahili text for sentiment analysis, validated through automated LLM judges. To ensure reliability, we conduct targeted human evaluation with a native Swahili speaker on a stratified sample, achieving 80.95% agreementbetween generated sentiment labels and human ground truth, with strong agreement on judge quality assessments. This demonstrates that LLM-based generation and quality assessment can transfer effectively to low-resource languages. We release a dataset and provide a reproducible pipeline in tandem, providing ample knowledge and working material for NLP researchers in low-resource contexts. We release the resulting Swahili sentiment dataset and the full reproducible generation pipeline publicly at https://huggingface.co/datasets/tabularisai/swahili-sentiment-dataset and https://github.com/tabularis-ai/Synthetic-Data-Generation-Pipeline-for-Low-Resource-Swahili-Sentiment-Analysis.

Despite remarkable progress in multilingual machine translation (MT), the majority of African—especially East African—languages remain significantly underrepresented both in benchmark datasets and state-of-the-art (SOTA) MT models. This persistent exclusion from mainstream technologies not only limits equitable access, but constrains the development of tools that accurately reflect the region’s linguistic and cultural diversity. Recent advances in open-source large language models have demonstrated strong multilingual MT capabilities through data-efficient adaptation strategies. However, little work has explored their potential for low-resource African languages. We introduce AfriMMT-EA, the first highly multilingual benchmark and MT dataset for East African languages. Our datasets comprise 54 local languages across five East African countries. We used these data to fine-tune two multilingual versions of Gemma-3. We compare models’ performance on these languages with larger off-the-shelf baselines. We release our data and models, in the interest of advancing MT for these low-resource languages and their communities.

This study examines the adoption, challenges, and impact of AI writing assistants in Tanzanian universities, with a focus on their role in supporting academic writing, enhancing accessibility, and accommodating low-resource languages such as Swahili. Through a structured survey of 1,005 university students, we analyze AI usage patterns, key barriers to adoption, and the improvements needed to make AI writing assistants more inclusive and effective. Findings reveal that limited Swahili integration, affordability constraints, and ethical concerns hinder AI adoption, disproportionately affecting students in resource-constrained settings. To address these challenges, we propose strategies for adapting AI models to diverse linguistic, academic, and infrastructural contexts, emphasizing Swahili-language support, AI literacy initiatives, and accessibility-focused AI development. By bridging these gaps, this study contributes to the development of AI-driven educational tools that are more equitable, contextually relevant, and effective for students in Tanzania and beyond.