Offline-First Quantized MamaBot AI: Design and Evaluation of an On-Device Maternal-Health Assistant for Low-Connectivity Environments

Abstract Offline language models could extend access to maternal-health information where internet connectivity is unreliable, but deployment requires both clinically appropriate responses and feasible local execution. This study presents an offline Android assistant integrating ready-made quantized language models, a clinically validated local knowledge package, conversational context, and model-initiated local retrieval. Gemma 4 E2B and Qwen3.5 0.8B were compared across 45 conversations and 312 turns per configuration, including a dedicated offline agentic benchmark. Gemma achieved higher response-quality, context-retention, and safety scores; Qwen offered faster generation and lower memory use. Clinician assessment of 150 Gemma responses across nine clinical domains yielded a mean quality score of 3.80/4, with 147 responses meeting the acceptance criterion. A paired RAG-on/off experiment evaluated 74 questions: retrieval improved 11 responses, left 56 neutral, produced four adverse response changes, and left three pairs unassessable. RAG-on had descriptively higher mean usefulness, safety, correctness, and actionability in the conditional 70-pair subset excluding the four flagged pairs from both conditions. Physical-device testing demonstrated offline responses on a 6 GB Android handset, with a 14.3 s median latency, alongside successful reconnection syn-chronization in the integrated application. Together, the findings document clinical response quality and practical performance of the integrated offline maternal-health assistant.