Generative artificial intelligence in K-12 education: A systematic review

Abstract

With the continuous innovation of deep learning algorithms, Generative Artificial Intelligence (GenAI) technology is rapidly developing globally and gradually expanding its application scenarios in multiple fields, especially in education. Considering the novelty of this field, there is currently a scarcity of comprehensive research on GenAI in K-12 education. Therefore, this systematic review aims to reveal the application trends, teaching themes, tool adoption, research methods, challenges, and advantages of generative artificial intelligence in K-12 education through the in-depth analysis of 45 studies between 2020 and 2024, providing theoretical and empirical support for future research and practice in this field. Our thematic analysis results indicate that GenAI tools can significantly improve students’ academic performance and cognitive abilities, enhance their learning motivation, and thus promote the development of personalized learning. However, using these tools also brings a series of challenges, including misleading or erroneous content generation, difficulty in understanding technology, students’ dependence on technology, and privacy infringement. In addition, the shortcomings demonstrated by educators in terms of AI literacy emphasize the necessity for relevant educational institutions to organize targeted AI literacy training. Finally, given that we are currently in the early stages of developing generative artificial intelligence, most existing empirical research has focused on the short-term impact of GenAI tools on K-12 education. Future research will incorporate more longitudinal studies to systematically evaluate the long-term and deep implications of GenAI in education.