Internet of Behaviour (IoB)-based AI models for personalized smart education systems

Abstract

E-learning, remote learning systems, and hybrid models were necessary in the pandemics age and when cities are rapidly moving towards smart city status. Adaptive and individualized education is now a must to reduce the drawbacks of distance learning while ensuring high levels of accomplishment. Technology such as machine learning, artificial intelligence (XAI), and data mining are all helping to transform education in a smart city by allowing for personalized learning and the ability to tailor content based on individual preferences. Based on the combination of XAI and IoB technologies, this study proposes a new paradigm for smart educational systems. Using data on students' actions, researchers try to establish whether or not contemporary educational systems meet students' needs. Education systems have evolved, but not to the point where they can be personalized to meet the cognitive demands of students and assist them when face-to-face instruction isn't available. Using evaluation methods, the study found that there has been a shift in students' academic progress when monitoring using IoT/IoB to enable a relative response in support of their progression. The system response to learners was 37%, once we involve the IoB in the processes and let students and the system be aware of the collected and used data about students' behaviours, the student's response improved to 76 % . This indicates the major influence of IoB on learner assistance and system adaptations to their real demands for higher achievement.