Visible to the public An adaptive paradigm for smart education systems in smart cities using the internet of behaviour (IoB) and explainable artificial intelligence (XAI)

TitleAn adaptive paradigm for smart education systems in smart cities using the internet of behaviour (IoB) and explainable artificial intelligence (XAI)
Publication TypeConference Paper
Year of Publication2022
AuthorsEmbarak, Ossama
Conference Name2022 8th International Conference on Information Technology Trends (ITT)
Date Publishedmay
Keywordsadaptive learning, distance learning, Electronic learning, Employment, Explainable Artificial Intelligence (XAI), future AI-based education, machine learning, Pandemics, pubcrawl, resilience, Resiliency, Scalability, Shape, smart cities, Smart cities education, Smart education, the internet of behaviour (IoB), xai
AbstractThe rapid shift towards smart cities, particularly in the era of pandemics, necessitates the employment of e-learning, remote learning systems, and hybrid models. Building adaptive and personalized education becomes a requirement to mitigate the downsides of distant learning while maintaining high levels of achievement. Explainable artificial intelligence (XAI), machine learning (ML), and the internet of behaviour (IoB) are just a few of the technologies that are helping to shape the future of smart education in the age of smart cities through Customization and personalization. This study presents a paradigm for smart education based on the integration of XAI and IoB technologies. The research uses data acquired on students' behaviours to determine whether or not the current education systems respond appropriately to learners' requirements. Despite the existence of sophisticated education systems, they have not yet reached the degree of development that allows them to be tailored to learners' cognitive needs and support them in the absence of face-to-face instruction. The study collected data on 41 learner's behaviours in response to academic activities and assessed whether the running systems were able to capture such behaviours and respond appropriately or not; the study used evaluation methods that demonstrated that there is a change in students' academic progression concerning monitoring using IoT/IoB to enable a relative response to support their progression.
DOI10.1109/ITT56123.2022.9863950
Citation Keyembarak_adaptive_2022