Vol.10, No.4, November 2021.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                          eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review


Aayat Aljarrah, Mustafa Ababneh, Damla Karagozlu, Fezile Ozdamli


© 2021 Aayat Aljarrah, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 10, Issue 4, Pages 1621-1629, ISSN 2217-8309, DOI: 10.18421/TEM104-18, November 2021.


Received: 02 August 2021.

Revised:  01 October 2021.
Accepted: 08 October 2021.
Published: 26 November 2021.




In the current era, education, like other fields, relies heavily on big data. Moreover, artificial intelligence, including affective computing, is one of the most essential and popular technologies adopted by educational institutions to process and analyze big data. In this systematic review, many previous research types related to improving educational systems using artificial intelligence techniques were studied, such as: deep learning, machine learning, and affective computing. This systematic review aims to identify the gaps in students' emotional understanding in distance education systems. The world has recently witnessed the spread of educational processes for distance learning, especially in the university and the enormous open online courses (MOOCs). Besides, the COVID-19 pandemic has been involved in changing all educational processes to a distance learning system. The results indicated that these systems recorded a high success rate. However, the teacher does not fully understand the student’s emotional state during the educational session. It also lacks monitoring or monitoring during the electronic exams, which are electronic exams. So, it is a widespread problem in distance learning.


Keywords –Face recognition; Machine learning; Convolutional Neural Network; Image processing; Emotion detection; Distance learning; artificial intelligence.



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