Artificial Intelligence in Elementary STEM Education: A Systematic Review of Current Applications and Future Challenges

TL;DR

This systematic review analyzes 258 recent studies on AI applications in elementary STEM education, highlighting current uses, effectiveness, gaps, and future challenges for integrating AI in early science, technology, engineering, and math learning.

Contribution

It provides a comprehensive synthesis of recent AI applications in elementary STEM education and identifies key gaps and future directions for research and practice.

Findings

Most studies focus on upper elementary grades and mathematics.

Conversational AI shows moderate effectiveness with effect sizes between 0.45 and 0.70.

Significant gaps include ecosystem fragmentation, privacy issues, and limited cross-disciplinary integration.

Abstract

Artificial intelligence (AI) is transforming elementary STEM education, yet evidence remains fragmented. This systematic review synthesizes 258 studies (2020-2025) examining AI applications across eight categories: intelligent tutoring systems (45% of studies), learning analytics (18%), automated assessment (12%), computer vision (8%), educational robotics (7%), multimodal sensing (6%), AI-enhanced extended reality (XR) (4%), and adaptive content generation. The analysis shows that most studies focus on upper elementary grades (65%) and mathematics (38%), with limited cross-disciplinary STEM integration (15%). While conversational AI demonstrates moderate effectiveness (d = 0.45-0.70 where reported), only 34% of studies include standardized effect sizes. Eight major gaps limit real-world impact: fragmented ecosystems, developmental inappropriateness, infrastructure barriers, lack of privacy frameworks, weak STEM integration, equity disparities, teacher marginalization, and narrow assessment scopes. Geographic distribution is also uneven, with 90% of studies originating from North America, East Asia, and Europe. Future directions call for interoperable architectures that support authentic STEM integration, grade-appropriate design, privacy-preserving analytics, and teacher-centered implementations that enhance rather than replace human expertise.