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

Majid Memari, Krista Ruggles

TL;DR

This paper addresses the fragmentation and uneven evidence base of AI in elementary STEM education by conducting a PRISMA‑guided review of 258 empirical studies from 2020 to 2025 across eight AI categories. It finds strong, domain‑specific gains for certain AI tools (notably conversational agents in math and science) but identifies persistent gaps in cross‑disciplinary integration, privacy, equity, teacher roles, and curriculum breadth, with most research concentrated in higher elementary grades and in North America, Europe, and East Asia. The authors propose an interoperable, privacy‑preserving, teacher‑centered architectural framework and a staged, ecosystem‑level research agenda that emphasizes AI–human collaboration, stakeholder engagement, and rigorous, cumulatively reportable evaluation standards. They argue that real progress requires moving beyond isolated technology studies to holistic, socio‑technical approaches that preserve human mentorship while leveraging AI to support authentic, career‑connected, and equity‑mfocused STEM learning. The work contributes a detailed map of current capabilities and limitations, practical pilot guidance, and a forward‑looking research program designed to translate AI innovations into scalable, responsible improvements in elementary STEM education.

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.

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

TL;DR

This paper addresses the fragmentation and uneven evidence base of AI in elementary STEM education by conducting a PRISMA‑guided review of 258 empirical studies from 2020 to 2025 across eight AI categories. It finds strong, domain‑specific gains for certain AI tools (notably conversational agents in math and science) but identifies persistent gaps in cross‑disciplinary integration, privacy, equity, teacher roles, and curriculum breadth, with most research concentrated in higher elementary grades and in North America, Europe, and East Asia. The authors propose an interoperable, privacy‑preserving, teacher‑centered architectural framework and a staged, ecosystem‑level research agenda that emphasizes AI–human collaboration, stakeholder engagement, and rigorous, cumulatively reportable evaluation standards. They argue that real progress requires moving beyond isolated technology studies to holistic, socio‑technical approaches that preserve human mentorship while leveraging AI to support authentic, career‑connected, and equity‑mfocused STEM learning. The work contributes a detailed map of current capabilities and limitations, practical pilot guidance, and a forward‑looking research program designed to translate AI innovations into scalable, responsible improvements in elementary STEM education.

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.

Paper Structure

This paper contains 65 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. The Promise of AI in Elementary STEM Education
  3. Unique Challenges in Elementary Settings
  4. Current State of the Field
  5. Research Questions and Purpose
  6. Materials and Methods
  7. Literature Search Strategy
  8. Inclusion and Exclusion Criteria
  9. Data Extraction and Synthesis
  10. Study Selection and PRISMA Flow
  11. Quality Appraisal and Risk of Bias
  12. Results
  13. Overview of AI Applications in Elementary STEM
  14. Intelligent Tutoring Systems and Conversational AI
  15. Assessment and Feedback Systems
  16. ...and 50 more sections

Figures (5)

  • Figure 1: PRISMA 2020 flow diagram for systematic review of AI applications in elementary STEM education.
  • Figure 2: Distribution of AI technology categories across included studies.
  • Figure 3: STEM domain coverage among included studies.
  • Figure 4: Grade-level coverage and STEM integration level among included studies. Left panel shows distribution between K–2 and grades 3–5 populations. Right panel indicates the limited integration across STEM domains, with 85% of studies focusing on single domains.
  • Figure 5: Geographic distribution of included studies.