TY - GEN
T1 - AI in Education
T2 - 14th IEEE Global Engineering Education Conference, EDUCON 2023
AU - Madathil, Nisha Thorakkattu
AU - Alrabaee, Saed
AU - Al-Kfairy, Mousa
AU - Damseh, Rafat
AU - Belkacem, Abdelkader N.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning outcomes and find solutions to improve the quality of their education system. Additionally, this work explores the application of ML in teaching and learning for further improvements in the learning environment for centralized and distributed education systems.
AB - Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning outcomes and find solutions to improve the quality of their education system. Additionally, this work explores the application of ML in teaching and learning for further improvements in the learning environment for centralized and distributed education systems.
KW - Education
KW - SDG
KW - federated learning
UR - http://www.scopus.com/inward/record.url?scp=85162659982&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85162659982&partnerID=8YFLogxK
U2 - 10.1109/EDUCON54358.2023.10125139
DO - 10.1109/EDUCON54358.2023.10125139
M3 - Conference contribution
AN - SCOPUS:85162659982
T3 - IEEE Global Engineering Education Conference, EDUCON
BT - EDUCON 2023 - IEEE Global Engineering Education Conference, Proceedings
PB - IEEE Computer Society
Y2 - 1 May 2023 through 4 May 2023
ER -