Farm and Learn: An Offline Mobile Learning System Integrating AR, AI, and Game-Based Learning for Agricultural Education Among Children

This study presents Farm and Learn, an offline-first mobile learning system that integrates Augmented Reality (AR), Artificial Intelligence (AI), and Game-Based Learning (GBL) to enhance agricultural education among children in low-connectivity environments. Existing agricultural learning applications often provide isolated functionalities such as visualization or plant recognition, with limited pedagogical integration and insufficient support for rural deployment. To address these limitations, the proposed system combines immersive AR-based exploration, interactive gamified learning activities, and AI-assisted paddy plant growth-stage identification within a unified child-centered educational framework. The architecture adopts a modular offline-first design that enables core learning functionalities to operate without continuous internet access while allowing optional synchronization when connectivity is available. The AI component employs a lightweight YOLOv11n deep learning model validated through prototype inference to assess feasibility for future on-device deployment. The system was developed using Unity and ARCore and evaluated through user acceptance testing involving students, educators, and domain experts. Results demonstrate high usability, strong learner engagement, and improved learning performance, confirming the effectiveness of integrating immersive visualization, intelligent interaction, and gamified reinforcement in educational contexts. The findings highlight the practical potential of offline-first mobile learning platforms to support inclusive agricultural education and provide a scalable foundation for future intelligent educational systems in resource-constrained environments.