2026 ASEE Annual Conference & Exposition

Learning AI Early: A Meta-Synthesis of Systematic Reviews on AI Education at the Elementary Level

Presented at Computers in Education (CoED): Learning, Engagement & Inclusion (5 of 9) -- T308B

In recent years, there has been a growing push for artificial intelligence (AI) education in response to the widespread impact of AI in society, particularly at the elementary education level (K-5). Many efforts focus on AI literacy, or teaching students the knowledge and critical awareness needed to understand technologies embedded in their everyday lives. A major goal is to inspire future interest in AI-related careers while supporting the development of informed and responsible citizens. Although K–12 learners are often treated as a homogeneous group, elementary students differ from older learners in developmental readiness and ability to absorb complex AI and machine learning ideas. Educators therefore must balance accessibility and conceptual rigor when designing AI learning experiences for younger audiences. Rather than conducting another systematic review, this paper draws on existing literature syntheses on elementary AI education to identify patterns, gaps, and tensions in how AI is introduced to elementary students. We coded these studies based on three questions: (1) How is AI literacy defined and operationalized in elementary education? (2) In what ways do these programs differ from traditional computing education initiatives? (3) What adjustments have been made to adapt AI literacy content for younger audiences? Findings from this meta-synthesis contribute to ongoing efforts to study AI education at the elementary level and offer guidance for researchers and instructors focused on AI literacy for younger learners.

Authors

Audrey Marie DeHoog http://orcid.org/https://0009-0006-2582-4163 Virginia Polytechnic Institute and State University [biography]

Audrey DeHoog has received a bachelor’s and a master’s degree in computer science from the University of Florida Herbert Wertheim College of Engineering in Gainesville, Florida. She is currently pursuing an engineering education PhD at Virginia Tech. She is interested in CS education, cross-cultural engineering, and interdisciplinary research.
Dr. Qin Zhu http://orcid.org/0000-0002-6673-1901 Virginia Polytechnic Institute and State University [biography]

Dr. Zhu is an Associate Professor in the Department of Engineering Education at Virginia Tech, with additional affiliations in the Department of Science, Technology & Society, the Department of Philosophy, the Center for Human-Computer Interaction, and the Center for Future Workplaces and Practices. He serves as Associate Editor for Science and Engineering Ethics, Studies in Engineering Education, and Editor for International Perspectives at the Online Ethics Center for Engineering and Science. Additionally, Dr. Zhu is a member of the Board of Directors for the Association for Practical and Professional Ethics (APPE). His research investigates how culture both shapes and is shaped by the formation of socially responsible engineers; the ethical design and deployment of emerging technologies such as AI, robotics, and quantum computing; and innovative approaches to teaching responsible AI and robot literacy in K–12 education. He directs the LANCSET (Laboratory for the Advancement of Normative, Cultural, and Societal Engagement with Emerging Technologies) Research Lab, where further information about his projects and publications is available.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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