Real-Time Content Personalization in Educational IoT Networks Using On-Device Learning

The use of Internet of Things (IoT) devices in the education sector has remarkably advanced the learning processes through personalized content delivery. In this paper, we develop real-time content personalization architecture design for Educational IoT (E-IoT) networks that utilize on-device learning techniques. The educational system is based on intelligent tablets, smartboards, and other wearables integrated with edge computing alongside federated learning models which modify the exposed teaching aids dynamically based on students’ behavioral data, preferences, and performance in real-time all while safeguarding privacy. On-device learning removes delays as well as the cloudcentric security threats which adaptive systems rely upon; providing rapid feedback loops and unending adjustments ensuring sustained relevance and engagement. This framework aims to operate effectively within the resource constraints of IoT devices and irregular network access. Simulated E-IoT classroom model optimized experiments showed improved content retention and learner engagement when exposed to personalized content as opposed to static content. This research highlights the advantages of combining edge intelligence with learning systems to enhance the flexibility of educational frameworks to evolving learner needs in real-time. The system aims to responsive pedagogical system requirements while guaranteeing privacy and scalability for smart educational systems.