Intelligent IoT‐Enabled Privacy‐Preserving Course Recommendation System

In the context of progressive education, artificial intelligence assumes a significant part in advancing personalized learning, providing individualized support, and fostering essential skill development to progress in today's dynamic society. Internet of Things (IoT)–enabled devices, such as smart speakers, wearable technology, and connected classroom tools, find personalized course recommendations to be indispensable tools that assist learners in navigating the huge array of learning resources available to them. Conventional course recommendation systems frequently rely on collaborative filtering or content-based approaches, but they often fall short of providing tailored support or focused attention. This emphasizes the need for creative methods to improve course recommendations, so federated learning and natural language processing (NLP) chatbots emerged, to meet the specific demands of students in an increasingly digitalized learning environment. This research offers novel solutions to this problem by combining federated learning methods with NLP-based chatbots to improve the course recommendation system's accuracy and privacy. An NLP-based chatbot can promote conversational and interactive engagements, giving students access to real-time support and direction. Moreover, federated learning guarantees collaborative model training across decentralized data sources while protecting privacy, enabling the creation of recommendation models that are more precise and customized. In this study, the data collected from various IoT-enabled educational devices, such as smart learning hubs and personal learning assistants, are analyzed to ensure a diverse and comprehensive dataset for training and evaluation. The result comprises two components: one is developing a chatbot to provide interactive support, whereas the other is putting federated learning into practice to provide individualized course recommendations. The process of implementing federated learning involves the use of complex algorithms such as federated averaging or federated optimization. These techniques protect data privacy, enable collaborative model training, and make it easier to coordinate model updates across decentralized devices. Similar to this, developing algorithms for activities such as response creation, dialogue management, and natural language interpretation is part of the deployment process for NLP chatbots. By facilitating conversational and interactive engagements with users, these algorithms improve user experience and offer support in real time.