%0 Journal Article %1 CHIU2023100118 %A Chiu, Thomas K.F. %A Xia, Qi %A Zhou, Xinyan %A Chai, Ching Sing %A Cheng, Miaoting %D 2023 %J Computers and Education: Artificial Intelligence %K Artificial Assessment Learning Systematic Teaching education intelligence review %P 100118 %R https://doi.org/10.1016/j.caeai.2022.100118 %T Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education %U https://www.sciencedirect.com/science/article/pii/S2666920X2200073X %V 4 %X Applications of artificial intelligence in education (AIEd) are emerging and are new to researchers and practitioners alike. Reviews of the relevant literature have not examined how AI technologies have been integrated into each of the four key educational domains of learning, teaching, assessment, and administration. The relationships between the technologies and learning outcomes for students and teachers have also been neglected. This systematic review study aims to understand the opportunities and challenges of AIEd by examining the literature from the last 10 years (2012–2021) using matrix coding and content analysis approaches. The results present the current focus of AIEd research by identifying 13 roles of AI technologies in the key educational domains, 7 learning outcomes of AIEd, and 10 major challenges. The review also provides suggestions for future directions of AIEd research.

@article{CHIU2023100118, abstract = {Applications of artificial intelligence in education (AIEd) are emerging and are new to researchers and practitioners alike. Reviews of the relevant literature have not examined how AI technologies have been integrated into each of the four key educational domains of learning, teaching, assessment, and administration. The relationships between the technologies and learning outcomes for students and teachers have also been neglected. This systematic review study aims to understand the opportunities and challenges of AIEd by examining the literature from the last 10 years (2012–2021) using matrix coding and content analysis approaches. The results present the current focus of AIEd research by identifying 13 roles of AI technologies in the key educational domains, 7 learning outcomes of AIEd, and 10 major challenges. The review also provides suggestions for future directions of AIEd research.}, added-at = {2023-05-01T16:59:43.000+0200}, author = {Chiu, Thomas K.F. and Xia, Qi and Zhou, Xinyan and Chai, Ching Sing and Cheng, Miaoting}, biburl = {https://www.bibsonomy.org/bibtex/2f382ec929e18b729f96003cc0729e8ee/yish}, doi = {https://doi.org/10.1016/j.caeai.2022.100118}, interhash = {99204082b4e4f45ff185ae7f77794be9}, intrahash = {f382ec929e18b729f96003cc0729e8ee}, issn = {2666-920X}, journal = {Computers and Education: Artificial Intelligence}, keywords = {Artificial Assessment Learning Systematic Teaching education intelligence review}, pages = 100118, timestamp = {2023-05-01T16:59:43.000+0200}, title = {Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education}, url = {https://www.sciencedirect.com/science/article/pii/S2666920X2200073X}, volume = 4, year = 2023 }