iTeachU: AI-Driven Tutor Matching with Geolocation and Personalised Analysis

Abstract

iTeachU mobile application is one of the applications developed to address the increasing number of requests for individual tutor matching in Malaysia, specifically in the Melaka region. The system will incorporate users' preferences and serve as a simplification tool to identify appropriate tutors based on preferences, as well as gender, subject, tutoring mode, and location. The application implements a hybrid recommendation strategy that combines content-based filtering and the K-Nearest Neighbours (KNN) algorithm, and is boosted by Haversine-based geofencing to find nearby tutors. In concurrence with conventional systems, iTeachU will also rank tutors according to a calculated Best Match and Distance Score, favouring classification based on their attributes rather than just the distance between tutors. The application has been created with Flutter on the front end and a Python (Flask) backend. Registration and preferences are to be saved in Firebase, and tutor details are to be stored in CSV files. Functional and usability tests showed that the app was very stable and user-friendly, and that it makes accurate recommendations by displaying the top matches. The iTeachU application shows what smart, location-based solutions can do to better integrate mobile support into the promotion of personalised educational assistance.