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Systematic review of research on artificial intelligence in K–12 education (2017–2022)
Open accessAbstract
The use of Artificial Intelligence (AI) has increased in all education sectors including K-12 settings where students can learn about AI and have an augmented learning experience using AI. The purpose of this systematic review is to provide a more complete and nuanced understanding of the role and impact of AI in K-12 education by synthesizing publication trends, AI research themes, AI methods and technology applications, and AI use by students and teachers in K-12 educational settings. The systematic review searched Web of Science and six databases indexed in EBSCO host. A PRISMA flow chart was applied to search and screen for studies. Articles were screened at the title, abstract and full-text level and coded and analyzed. Themes in 66 AI studies include AI as a predictor and indicator of academic behavior or performance, AI curriculum design, integrating AI in various subjects, evaluation of AI in education, AI to enhance learning environments and school operations, AI ethics, and the equity and safety of AI. AI methods were grouped into Supervised Learning, Unsupervised Learning and Reinforcement Learning. AI technology applications were Machine Learning (ML) model building tools, intelligent tutors, chat bot, educational games, AI robots and virtual reality devices. AI applications were mostly used by teachers for ML model demonstration, academic performance prediction and behavior prediction. AI was used by students for scientific discovery learning, improving learning experience and data driven decisions. This review has implications for K-12 school personnel and researchers. Practitioners can use the findings to implement AI in K-12 education. Researchers can benefit from the findings of the review but also build on the gap in research on AI K-12 education.
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“A separate stream of reviews surveys GenAI's administrative, technical, and outcome-focused uses in teaching [35-38].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [35]
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Kerimbayev, N., Akramova, А., Shadiev, R., & Altınay, Z. (2025) · Smart Learning Environments · doi:10.1186/s40561-024-00360-3 · Cited by 110
“Martin et al. (2023) outlined six themes regarding the implementation of intelligence, in their research findings.”
Metadata
- Authors
- Martin, F., Zhuang, M., & Schaefer, D.
- Affiliations
- North Carolina State University (US)
- University of North Carolina at Charlotte (US)
- Corresponding author
- Martin, Florence
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Pages
- 100195
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4390154952
- Semantic Scholar
- b3e704df385ec074a58d6468ac60746cd16a2178
- Cited by
- 132
- References
- 90
- Citations by year
- 2026: 46 · 2025: 70 · 2024: 16
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, K-12 education, student performance prediction, student behavior prediction, AI curriculum design, intelligent tutoring systems, chatbots, game-based learning, AI robots, virtual reality in education, AI ethics, supervised learning, unsupervised learning, reinforcement learning, machine learning in education, teacher use of AI, student use of AI
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