Offline AI-Driven Low-Resource Multilingual Tutoring Architecture For Rural Education

This research presents OAMTS (Offline AI-Driven Multilingual Tutoring System), a low-resource AI tutoring architecture designed for rural educational environments with limited or no internet connectivity. The proposed framework integrates lightweight machine learning models, multilingual NLP processing, adaptive recommendation systems, local educational databases, and offline synchronization mechanisms to enable personalized AI-assisted learning on low-specification hardware.   The paper focuses on architectural design, deployment feasibility, and simulation-based evaluation using educational analytics derived from publicly available datasets including UDISE+, AISHE, and TRAI reports. The proposed system aims to address structural educational challenges such as high student-teacher ratios, multilingual accessibility barriers, low digital infrastructure availability, and educational inequality in underserved rural regions.   This manuscript is published as a research preprint and architecture design proposal intended to support future prototype development, field validation, and scalable AI-assisted educational research.