Purpose
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.
Methods
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.
Results
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.
Conclusion
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.
