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

arXiv: https://arxiv.org/abs/2511.00105 · HTML: https://mlsanity.com/paper/2511.00105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 111 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Utility of Optical See-Through Head Mounted Displays in Augmented Reality-Assisted Surgery: A systematic review](https://mlsanity.com/paper/2202.04141/) (2022)

## Abstract (arXiv)

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.