Design and Implementation of an Intelligent Education Assistance System Based on Internet of Things and Artificial Intelligence

With rapid technological advancement, education has faced a tremendous influence from technological advancement, with new opportunities for enhanced learning experiences. This research develops an Intelligent Education Assistance System (IEAS) based on the Internet of Things (IoT) and Artificial Intelligence (AI) technologies with a focus on improving material assistance within the context of an educational environment. IEAS comprises four different layers, such as the data collection layer, processing layer, continuous monitoring layer, educator support and feedback layer. This system utilizes IoT devices to gather real-time data from the classroom and its surroundings. Advanced pre-processing techniques, including Z-score normalization (ZSN) and data cleaning, are implemented to ensure that the collected data from different IoT devices present in the classroom is precise and of high quality. It extracts the features using Discrete Wave Transform (DWT). The data are then fed into a new Intelligent Reptile Search Optimized Feed Forward Neural Network (IRSO-FFNN) to optimize material distribution and give actionable insights for educators and students. Optimized by the Reptile Search, FFNN is fine-tuned by its weights to predict the dynamic needs of the students as well as personalize the learning assistance. The proposed system monitors student engagement, learning progression, and material requirements in the implementation phase by integrating smart devices with interactive interfaces for immediate help. The IEAS model achieves impressive performance metrics by reporting a precision of 98.8%, F1-score of 99%, recall of 98.9%, and accuracy of 99.1%. To demonstrate the transformative capability of IoT and AI in creating smarter, adaptive environments for learning, transforming how learning content is delivered and managed in the educational domain.