Generative AI use in K-12 education: a systematic review

Abstract

As generative artificial intelligence (GenAI) continues to generate interest and impact all levels of the educational system, including K-12, it is essential to understand how GenAI has been utilized at the K-12 level. This study explored the literature to understand what GenAI has been used for in K-12 and the reported impacts of its use. By investigating two prominent databases, Scopus and Web of Science, and employing snowballing techniques, this study included 30 papers after a rigorous search and applying inclusion and exclusion criteria. The findings indicate that most existing works focused on high school and preservice teachers. Researchers have explored various subject domains or areas of interest where GenAI has been used, with a significant focus on STEM-related subjects. The quasi-experimental approach emerged as the most commonly used design technique for implementing GenAI ideas in the classroom. Psychological variables were the primary measures used to gauge learning outcomes. This study further aggregates recommendations from prior works and suggests areas for future research. This paper contributes to efforts in creating frameworks and building resources needed to realize the full potential of GenAI in shaping the educational landscape, especially within the school system.

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