Designing an AI Model for Learning Device Identification in Classrooms

Creating and maintaining order in classroom resources is a particular difficulty that teachers face in today's contemporary education systems.This paper focuses on solving the problem concerning the automated recognition of educational devices by creating an image recognition model based on deep learning techniques.The model employs Convolutional Neural Networks (CNNs) and transfer learning for optimal identification performance.The model was trained and tested using a dataset containing images of four distinct types of educational devices.Based on the experiments, the model could analyze ordinary images with over 90% accuracy and at almost real-time speeds.The system architecture, dataset preparation, and assessment methods are described.The evaluations' high accuracy and low computation time underline the model's versatility, making it applicable to analyze use patterns, tracking resources, and inventory control in education.The computer vision applications in education are expanded with this research, enhancing the efficiency of classroom automation processes.Also covered are the possible extensions for the model's capabilities and how it may be connected to other existing educational systems.