The DOI matches its Crossref record; 87 of the 90 references it cites were found in a registry or an index.
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]
Citations
Cites
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972 · cited 1×
“Reinforcement learning is a type of ML which enables software agents and machines to automatically evaluate the optimal behavior in a particular context or environment to improve its efficiency (Kaelbling, 1996).”
(2006) · Springer eBooks · Cited by 8,123
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583 · cited 6×
“Some common algorithms are K-means clustering and Mean-shift clustering (Sarker, 2021).”
InfluentialMachine learning and deep learning
Janiesch, C., Zschech, P., & Heinrich, K. (2021) · Electronic Markets · doi:10.1007/s12525-021-00475-2 · Cited by 2,959 · cited 6×
“Because of their flexibility structure, which enables them to be adapted and modified for a wide range of contexts across all three types of ML (supervised learning, un-supervised learning, and reinforcement learning) (Janiesch, et al., 2021).”
InfluentialProceedings of the 2020 CHI Conference on Human Factors in Computing Systems
(2020) · doi:10.1145/3313831 · Cited by 1,951
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · cited 2×
“Comparable literature reviews including the Zhai et al. (2021) systematic review have focused on types of methodology, but one distinguishing feature of this systematic review is it not only includes study method, quantitative, qualitative, and mixed method, but it also coded studies according to…”
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100118 · Cited by 1,181
The promises and challenges of artificial intelligence for teachers: A systematic review of research
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022) · TechTrends · doi:10.1007/s11528-022-00715-y · Cited by 984 · cited 2×
“Celik et al. (2022) in their review found that teachers used AI for planning such as understanding student needs, for implementation such as providing immediate feedback, and for assessment such as automated essay scoring.”
Research works printed but not linked: 3
- Bilstrup, 2020, Staging reflections on ethical dilemmas in machine learning: A card-based design workshop for high school students, 1211
- Ghareeb Ahmed Ali, 2020, Using an artificial intelligence application for developing primary school pupils', Oral Language Skills, 75, 67
- Zhang, 2021, AI technologies for education: Recent research & future directions, Computers & Education: Artificial Intelligence, 2
Cited by
Sorted by citations
The ethics of using AI in K-12 education: a systematic literature review
Gouseti, A., James, F., Fallin, L., & Burden, K. (2025) · Technology, Pedagogy and Education · doi:10.1080/1475939x.2024.2428601 · Cited by 141
“Furthermore, Martin et al. (2024) report in their systematic review that students used AI for scientific discovery learning and improving their learning experience.”
InfluentialTeachers and AI: Understanding the factors influencing AI integration in K-12 education
Filiz, O., Kaya, M. H., & Adıgüzel, T. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13463-2 · Cited by 243
“…intelligence in education (AIEd) denotes the application of AI technologies, such as intelligent tutoring systems, chatbots, robots, and automated assessment tools, across various digital platforms to enhance educational performance across subjects (Chiu et al., 2023; Martin et al., 2024).”
Critical Artificial Intelligence literacy: A scoping review and framework synthesis
Veldhuis, A., Lo, P., Kenny, S., & Antle, A. N. (2024) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2024.100708 · Cited by 153
Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration
Kohnke, L., Zou, D., Ou, A. W., & Gu, M. M. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100398 · Cited by 86
Intelligent educational technologies in individual learning: a systematic literature review
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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