A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda

Abstract Despite the increased adoption of Artificial Intelligence in Education (AIED), several concerns are still associated with it. This has motivated researchers to conduct (systematic) reviews aiming at synthesizing the AIED findings in the literature. However, these AIED reviews are diversifie...

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Main Authors: Muhammad Yasir Mustafa, Ahmed Tlili, Georgios Lampropoulos, Ronghuai Huang, Petar Jandrić, Jialu Zhao, Soheil Salha, Lin Xu, Santosh Panda, Kinshuk, Sonsoles López-Pernas, Mohammed Saqr
Format: Article
Language:English
Published: SpringerOpen 2024-12-01
Series:Smart Learning Environments
Subjects:
Online Access:https://doi.org/10.1186/s40561-024-00350-5
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author Muhammad Yasir Mustafa
Ahmed Tlili
Georgios Lampropoulos
Ronghuai Huang
Petar Jandrić
Jialu Zhao
Soheil Salha
Lin Xu
Santosh Panda
Kinshuk
Sonsoles López-Pernas
Mohammed Saqr
author_facet Muhammad Yasir Mustafa
Ahmed Tlili
Georgios Lampropoulos
Ronghuai Huang
Petar Jandrić
Jialu Zhao
Soheil Salha
Lin Xu
Santosh Panda
Kinshuk
Sonsoles López-Pernas
Mohammed Saqr
author_sort Muhammad Yasir Mustafa
collection DOAJ
description Abstract Despite the increased adoption of Artificial Intelligence in Education (AIED), several concerns are still associated with it. This has motivated researchers to conduct (systematic) reviews aiming at synthesizing the AIED findings in the literature. However, these AIED reviews are diversified in terms of focus, stakeholders, educational level and region, and so on. This has made the understanding of the overall landscape of AIED challenging. To address this research gap, this study proceeds one step forward by systematically meta-synthesizing the AIED literature reviews. Specifically, 143 literature reviews were included and analyzed according to the technology-based learning model. It is worth noting that most of the AIED research has been from China and the U.S. Additionally, when discussing AIED, strong focus was on higher education, where less attention is paid to special education. The results also reveal that AI is used mostly to support teachers and students in education with less focus on other educational stakeholders (e.g. school leaders or administrators). The study provides a possible roadmap for future research agenda on AIED, facilitating the implementation of effective and safe AIED.
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spelling doaj-art-462fa4163b8a4e0fbd152f259f6652b02024-12-15T12:13:29ZengSpringerOpenSmart Learning Environments2196-70912024-12-0111113310.1186/s40561-024-00350-5A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agendaMuhammad Yasir Mustafa0Ahmed Tlili1Georgios Lampropoulos2Ronghuai Huang3Petar Jandrić4Jialu Zhao5Soheil Salha6Lin Xu7Santosh Panda8Kinshuk9Sonsoles López-Pernas10Mohammed Saqr11Chengdu University of TechnologySmart Learning Institute of Beijing Normal UniversityDepartment of Applied Informatics, University of MacedoniaSmart Learning Institute of Beijing Normal UniversityZagreb University of Applied SciencesStanford UniversityDepartment of Education, An-Najah National UniversitySmart Learning Institute of Beijing Normal UniversityIndira Gandhi National Open UniversityCollege of Information, University of North TexasUniversity of Eastern FinlandUniversity of Eastern FinlandAbstract Despite the increased adoption of Artificial Intelligence in Education (AIED), several concerns are still associated with it. This has motivated researchers to conduct (systematic) reviews aiming at synthesizing the AIED findings in the literature. However, these AIED reviews are diversified in terms of focus, stakeholders, educational level and region, and so on. This has made the understanding of the overall landscape of AIED challenging. To address this research gap, this study proceeds one step forward by systematically meta-synthesizing the AIED literature reviews. Specifically, 143 literature reviews were included and analyzed according to the technology-based learning model. It is worth noting that most of the AIED research has been from China and the U.S. Additionally, when discussing AIED, strong focus was on higher education, where less attention is paid to special education. The results also reveal that AI is used mostly to support teachers and students in education with less focus on other educational stakeholders (e.g. school leaders or administrators). The study provides a possible roadmap for future research agenda on AIED, facilitating the implementation of effective and safe AIED.https://doi.org/10.1186/s40561-024-00350-5Artificial intelligenceGenerative AIEducationSmart learningLiterature reviewMeta-synthesis
spellingShingle Muhammad Yasir Mustafa
Ahmed Tlili
Georgios Lampropoulos
Ronghuai Huang
Petar Jandrić
Jialu Zhao
Soheil Salha
Lin Xu
Santosh Panda
Kinshuk
Sonsoles López-Pernas
Mohammed Saqr
A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
Smart Learning Environments
Artificial intelligence
Generative AI
Education
Smart learning
Literature review
Meta-synthesis
title A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
title_full A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
title_fullStr A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
title_full_unstemmed A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
title_short A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
title_sort systematic review of literature reviews on artificial intelligence in education aied a roadmap to a future research agenda
topic Artificial intelligence
Generative AI
Education
Smart learning
Literature review
Meta-synthesis
url https://doi.org/10.1186/s40561-024-00350-5
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