Offline AI-Based Personalized Learning Application For Rural Area Students

Author(s)	Ms. Alefiya Mustafa Poonawala, Prof. Shakila Siddavatam
Country	India
Abstract	This research addresses the challenges faced by rural students in accessing quality education due to poor internet connectivity, limited digital infrastructure, and lack of personalized learning support. The study proposes an offline AI-based personalized learning application designed specifically for rural area students. The system uses lightweight artificial intelligence models that run directly on the learner’s device to adapt educational content according to the student’s performance, learning pace, and understanding level. By processing data locally, the application ensures uninterrupted learning, data privacy, and smooth performance even on low-end devices without continuous internet access. The platform enables students to study at their own pace while receiving personalized learning recommendations. Experimental observations indicate that personalized offline learning improves student engagement and academic performance compared to traditional non-personalized methods. The study demonstrates that offline AI-based personalized learning systems can serve as an effective and inclusive solution to bridge the educational gap between rural and urban learners.
Keywords	Rural Education, Personalized Learning, Offline Artificial Intelligence, Adaptive Learning, Educational Technology
Field	Computer Applications
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-13

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International Journal For Multidisciplinary Research