Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review
Open accessAbstract
Education is a vital part of the development of society and it is changing over time in terms of methods, content, concepts, and models. Recently, it has been increasingly prevalent to benefit from the potentialities of Artificial Intelligence (AI) in addressing educational issues. In this research, the current state of the art of the integration of AI in K-12 education was provided. Specifically, different parts of education in which AI was employed along with the related AI categories were discussed according to different K-12 grades and courses. Additionally, technologies and environments that contributed to employing AI in education were discussed. To this end, a systematic literature review was conducted on articles and conference papers published between 2011 and 2021 in the Web of Science and Scopus databases. As the result of the initial search, 2075 documents were extracted and based on inclusive criteria and 210 documents were identified for further investigation. AI applications were categorized into Student performance, Teaching, Selection, and Behavior tasks, and Other. Machine Learning (ML) and Intelligent Tutoring System (ITS) were the most common approaches among AI categories. Furthermore, high school-related applications were more frequent and STEM courses were substantially targeted by AI. In conclusion, the remarkable impact of AI on education was concluded. The current study reveals information about the potentialities offered by AI in K-12 education which aids researchers in implementing AI-based education systems. As for future works, other databases such as ACM library and Google Scholar can be investigated as well. Furthermore, exploring the 95 papers that were excluded due to inaccessibility to their full texts can be taken into account. Finally, the papers can be also investigated in terms of pedagogical approaches or development tools.
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In a systematic review of AI literature in education, tools reviewed included intelligent tutoring, machine learning, language processing, robotics, data mining, and deep learning.
“In a systematic review of the AI literature in education, tools reviewed included intelligent tutoring, machine learning, language processing, robotics, data mining, and deep learning (Zafari et al., 2022).”
From The Future of Artificial Intelligence in Special Education Technology (2023) · cites this paper as Zafari et al., 2022
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Flagged first, then by citations
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- Bannerjee, G., et al., Artificial intelligence in agriculture: A literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 2018. 7(3): p. 1-6.
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- Authors
- Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S.-M.
- Affiliations
- Graduate University of Advanced Technology (IR)
- Sejong University (KR)
- Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, South Korea
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 61905-61921
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4285222038
- Cited by
- 185
- References
- 135
- Citations by year
- 2026: 50 · 2025: 61 · 2024: 54 · 2023: 16 · 2022: 4
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Trade, Industry and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) through the International Cooperative Research and Development Program (P0016038)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- artificial intelligence in education, K-12 education, machine learning in education, intelligent tutoring systems, student performance, student selection, student behavior, STEM education, upper secondary school
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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