Date
Publisher
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.
Study at a glance
- Question: How are AI technologies used in elementary STEM education, how effective are they, and what gaps remain?
- Setting and data: Synthesis of 258 peer-reviewed empirical studies published between 2020 and 2025 focusing on elementary students (ages 5–12).
- Method: Systematic review following PRISMA guidelines with thematic synthesis and quality appraisal using MMAT.
Why it matters
Artificial intelligence is rapidly transforming elementary STEM education, yet evidence remains fragmented across isolated subject areas and tools, lacking comprehensive evaluation frameworks.
Key findings
- Most studies focus on upper elementary grades (65%), mathematics (38%), and intelligent tutoring systems (45%), with only 15% attempting cross-disciplinary STEM integration.
- Conversational AI demonstrates moderate learning effectiveness (d = 0.45 to 0.70 where reported), but only 34% of studies report standardized effect sizes.
- Real-world impact is limited by eight systemic gaps, notably fragmented ecosystems, developmental mismatches between K–2 and older grades, infrastructure barriers, and teacher marginalization.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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