An Efficient Framework for Intelligent Learning Based on Artificial Intelligence and IoT

Authors

  • Mohammed Ateeq Alanezi Computer Science Department, College of Computer Science and Engineering, University of Hafr Al Batin

DOI:

https://doi.org/10.3991/ijet.v17i07.27851

Keywords:

Artificial Intelligence, Internet of Things, E-Learning, Smart learning, Artificial Neural Networks

Abstract


Learning based applications have become smarter with the emergence of Internet of Things (IoT), and the devices which were connected with it give rise to their exploitation in all facets of a modern education. As the volume of data increases, IoT-based techniques are applied to further enhancing an application's knowledge and capabilities. Many researchers have been drawn to the area of smart learning using both the Internet of Things (IoT) and Agent based system. The main aim of the learning management is to make the students and its teachers to develop skills, use and implement the technologies in a scenario that produces advanced outcomes in learning process. There are many technologies which help the exposure of smart education. In this paper, a systematic review is carried out on the applications of Artificial Intelligence (AI) in the learning management. Various AI systems used in the field of application in learning management were analyzed. A novel framework based on a proposed architecture and its implementation requirements of the AI and IoT based Learning management system is also discussed.

Author Biography

Mohammed Ateeq Alanezi, Computer Science Department, College of Computer Science and Engineering, University of Hafr Al Batin

Mohammed Ateeq Alanezi() Computer Science Department College of Computer Science and Engineering, University of Hafr Al Batin

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Published

2022-04-12

How to Cite

Ateeq Alanezi, M. (2022). An Efficient Framework for Intelligent Learning Based on Artificial Intelligence and IoT. International Journal of Emerging Technologies in Learning (iJET), 17(07), pp. 112–124. https://doi.org/10.3991/ijet.v17i07.27851

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Section

Papers