Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education

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,...

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Vydané v:Computers and education. Artificial intelligence Ročník 4; s. 100118
Hlavní autori: Chiu, Thomas K.F., Xia, Qi, Zhou, Xinyan, Chai, Ching Sing, Cheng, Miaoting
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 2023
Elsevier
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ISSN:2666-920X, 2666-920X
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Popis
Shrnutí: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. •Applications of artificial intelligence in education (AIEd) are emerging and new to researchers and practitioners.•AIEd research is more scattered and less organized.•This review is to understand the opportunities and challenges of AIEd in four educational domains and two learning outcomes.•The results presented 13 roles of AI technologies in the domains, and seven outcomes of AIEd.•We provided 10 challenges and their recommendations for future research in AIEd.
ISSN:2666-920X
2666-920X
DOI:10.1016/j.caeai.2022.100118