Proactive and reactive engagement of artificial intelligence methods for education: a review
The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2023.1151391/full |
| _version_ | 1797833064052686848 |
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| author | Sruti Mallik Ahana Gangopadhyay |
| author_facet | Sruti Mallik Ahana Gangopadhyay |
| author_sort | Sruti Mallik |
| collection | DOAJ |
| description | The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education. |
| first_indexed | 2024-04-09T14:17:44Z |
| format | Article |
| id | doaj.art-b75eaaa698ed4d33968a55e2d934582a |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-09T14:17:44Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-b75eaaa698ed4d33968a55e2d934582a2023-05-05T06:09:51ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122023-05-01610.3389/frai.2023.11513911151391Proactive and reactive engagement of artificial intelligence methods for education: a reviewSruti MallikAhana GangopadhyayThe education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.https://www.frontiersin.org/articles/10.3389/frai.2023.1151391/fullartificial intelligence applications (AIA)artificial intelligence for education (AIEd)technology enhanced learningmachine learningartificial intelligence for social good (AI4SG) |
| spellingShingle | Sruti Mallik Ahana Gangopadhyay Proactive and reactive engagement of artificial intelligence methods for education: a review Frontiers in Artificial Intelligence artificial intelligence applications (AIA) artificial intelligence for education (AIEd) technology enhanced learning machine learning artificial intelligence for social good (AI4SG) |
| title | Proactive and reactive engagement of artificial intelligence methods for education: a review |
| title_full | Proactive and reactive engagement of artificial intelligence methods for education: a review |
| title_fullStr | Proactive and reactive engagement of artificial intelligence methods for education: a review |
| title_full_unstemmed | Proactive and reactive engagement of artificial intelligence methods for education: a review |
| title_short | Proactive and reactive engagement of artificial intelligence methods for education: a review |
| title_sort | proactive and reactive engagement of artificial intelligence methods for education a review |
| topic | artificial intelligence applications (AIA) artificial intelligence for education (AIEd) technology enhanced learning machine learning artificial intelligence for social good (AI4SG) |
| url | https://www.frontiersin.org/articles/10.3389/frai.2023.1151391/full |
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