AI-Powered Microlearning for Rural Education in Low-Bandwidth Areas

Rural education in low-bandwidth regions faces significant barriers including poor connectivity, lack of qualified teachers, and inadequate infrastructure. This paper proposes an AI-driven microlearning platform tailored for offline environments using low-cost edge devices. By leveraging a Raspberry Pi, open-source language models, and multilingual content, we offer a localized, scalable solution for remote learning. Our pilot implementation across three government schools in rural Karnataka showed a 38% improvement in student comprehension and retention. The findings support AI- powered microlearning as a transformative tool for inclusive, accessible education in underserved communities. Keywords—Microlearning, AI in Education, Edge Computing, Rural Development, Low-Bandwidth Learning, Offline Learning Systems