How Does AI Impact Student Learning? An Umbrella Review of Promises, Pitfalls, and Research Gaps

• In Event: AERA Poster Session Saturday 9:45 am
  In Poster Session: AI and Technology Integration in K-12 Education

Abstract

This umbrella review approach synthesized findings from 40 prior review articles to examine the impact of AI tools on student learning. Using the PRISMA guidelines, we analyzed reported promises, pitfalls, and research gaps across various educational levels, disciplines, and types of AI tools. Results revealed consistent evidence that AI can improve learning outcomes, particularly in language learning and through personalized or text-based systems. However, many studies also noted limited or inconsistent effects, particularly across different contexts and designs. Human instruction remains irreplaceable in many domains. Substantial gaps include limited attention to diverse learners, overemphasis on certain subjects, and methodological weaknesses. This review provides a strategic overview to inform future research and guide the responsible integration of AI in education.

Authors

• Qiyang Lin, Michigan State University

• Fangzheng Zhao, Nanjing Normal University

• Yuli Shao, New York University

• Amédee Marchand Martella, University of Georgia