Handwritten equation detection in disconnected, low-cost mobile devices

Artificial Intelligence in Education (AIED) implementation in underserved regions faces challenges due to limited digital infrastructure, such as restricted device and internet access. A solution to these challenges lies in AIED Unplugged, a framework designed to address these challenges by tailoring AI solutions to the specific issues prevalent in such regions. AIED Unplugged incorporates principles like Conformity, Disconnect, Proxy, Multi-User, and Unskillfulness, ensuring accessibility by aligning with existing infrastructure, operating offline, simplifying interfaces, and accommodating users' digital skills. Particularly, the framework leverages computer vision to digitalize students' activities and enable AIED-based learning on disconnected, low-cost devices, wherein object detection is crucial to identify which solution areas to digitalize. However, prior research has not assessed the technical feasibility of such applications in the context of AIED unplugged for math education. Therefore, this paper addresses the intersection of "conformity" and "disconnected" principles with an empirical analysis of handwritten equation detection on disconnected, low-cost mobile devices. By optimizing state-of-the-art algorithms for offline inference and considering device constraints, we utilize a dataset of student equations, explore YOLOv8 models, and evaluate its predictive performance. The trained model is converted to Tensorflow Lite for mobile deployment, and a testbed application assesses inference times on diverse low-cost devices, contributing valuable empirical insights to the intersection of AIED Unplugged, Computer Vision, and Education in underserved regions.