Artificial intelligence in K-12 education: An umbrella review

This umbrella review examines the current state of artificial intelligence in education (AIEd) in K-12 by synthesizing 102 systematic reviews. We described their characteristics, built a framework to summarize the review foci, synthesized the major findings, and examined their quality. The main findings are as follows: (a) AIEd Review Framework: We developed a framework that maps the territory of AIEd to understand the research topics distributed and interconnected. (b) Innovation: AI has been applied to support teachers’ instruction, personalize learning, enhance engagement and collaboration, automate assessment and feedback, and manage educational content, reflecting its multifaceted potential in education. (c) Synthesized Knowledge: The reviews reveal how AI is developed, applied, and evaluated in education, with major challenges including technological limitations, pedagogical hurdles, ethical risks, and systemic barriers. (d) AI Education and Literacy: More reviews are needed on AI education and literacy. Compared with the rapid advancement of AI applications, preparing teachers and students to understand and ethically use AI has received much less attention. (e) AIEd Theories: More reviews are needed for theoretical development for AIEd. Most studies apply existing frameworks descriptively rather than empirically testing or extending them. (f) Quality Assessment: We developed a rubric to assess systematic reviews and found gaps in data management, extraction, analysis, and transparency. Overall, this umbrella review maps the territory of AIEd, identifies underexplored areas and future directions. The AIEd Review Framework can be used to guide future reviews in AIEd. The quality assessment rubric can be used for future umbrella reviews in general.