🤖 AI Summary
This study addresses eight critical gaps in AI integration within elementary STEM education: fragmented empirical evidence, insufficient interdisciplinary integration, geographic imbalance, and deficiencies in privacy protection and equity. A systematic review of 258 empirical studies published between 2020 and 2025 identifies key trends: 65% focus on upper elementary grades; 38% concentrate exclusively on mathematics; only 15% achieve authentic interdisciplinary STEM integration; and merely 34% report standardized effect sizes—while geographic representation remains heavily skewed toward North America, East Asia, and Europe. To bridge these gaps, the study proposes a novel four-dimensional technical framework—centered on *teacher agency*, *developmentally appropriate design*, *privacy-by-design*, and *STEM disciplinary integration*. It synthesizes seven AI-enabled pedagogical pathways: intelligent tutoring systems, learning analytics, computer vision, educational robotics, multimodal sensing, AI-augmented extended reality (XR), and adaptive content generation—advancing AI’s role in education from isolated tool deployment to holistic, ecosystem-level reconstruction.
📝 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.