The Role of AI in Early Educational Settings : A Systematic Review with a Methodological Exploration of an AI-Assisted Review Workflow
- 1Ontario Institute for Studies in Education, University of Toronto, Toronto, ON, Canada
- 2University of Guelph, Guelph, ON, Canada
- 3University of Toronto, Toronto, ON, Canada
Abstract
Background: Early educational settings, such as child care, preschool, kindergarten, and early school classrooms, are important contexts for supporting young children’s development. Artificial intelligence (AI)-based activities are being introduced into these settings, yet evidence on how they are used and what developmental outcomes their use is associated with remains limited.
Objectives: This review provides a narrative synthesis of the empirical studies examining AI-based activities in early educational settings, using an AI-assisted workflow to support data extraction during evidence synthesis.
Methods: We identified 13 empirical studies through searches of seven academic databases, including PsycINFO and ERIC. Information was extracted on study characteristics, AI-based tools and activities, and child developmental outcomes. Given heterogeneity in study designs, tool and activity types, and child outcomes, findings, including statistical ones, were synthesized results narratively.
Results: Diverse tools were identified, including Speech recognition Systems (n = 5), AI-Powered Robots (n = 4), Adaptive Learning Systems (n = 4), AI-Enhanced Extended reality (n = 3), and Conversational AI and Chatbot (n = 2), demonstrating potential benefits across cognitive, language, literacy, social, emotional, and fine motor domains. Most associations were found between AI and children’s language (n = 5) and literacy (n = 3).
Author’s Conclusions: AI-based activities in early educational settings show preliminary promise, particularly for language and literacy, but the evidence base is limited and heterogeneous. More rigorous studies are needed to examine developmental outcomes, equity, safety, and sustained effects. Researchers should specifically address equity issues and potential adverse effects when conducting research in this area.
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BibTeX
@article{yu2026role,
title = {{The Role of AI in Early Educational Settings : A Systematic Review with a Methodological Exploration of an AI-Assisted Review Workflow}},
author = {Esther Yu and Samantha Burns and Joel P. Wiebe and Maria Tsiokos and Emma Pancham and Michal Perlman},
journal = {Campbell systematic reviews},
year = {2026},
month = sep,
doi = {10.1177/18911803261486466},
url = {https://europepmc.org/article/PMC/PMC13724888},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - The Role of AI in Early Educational Settings : A Systematic Review with a Methodological Exploration of an AI-Assisted Review Workflow
AU - Esther Yu
AU - Samantha Burns
AU - Joel P. Wiebe
AU - Maria Tsiokos
AU - Emma Pancham
AU - Michal Perlman
PY - 2026
DA - 2026/09/01
JO - Campbell systematic reviews
DO - 10.1177/18911803261486466
UR - https://europepmc.org/article/PMC/PMC13724888
AB - Background: Early educational settings, such as child care, preschool, kindergarten, and early school classrooms, are important contexts for supporting young children’s development. Artificial intelligence (AI)-based activities are being introduced into these settings, yet evidence on how they are used and what developmental outcomes their use is associated with remains limited. Objectives: This review provides a narrative synthesis of the empirical studies examining AI-based activities in early educational settings, using an AI-assisted workflow to support data extraction during evidence synthesis. Methods: We identified 13 empirical studies through searches of seven academic databases, including PsycINFO and ERIC. Information was extracted on study characteristics, AI-based tools and activities, and child developmental outcomes. Given heterogeneity in study designs, tool and activity types, and child outcomes, findings, including statistical ones, were synthesized results narratively. Results: Diverse tools were identified, including Speech recognition Systems (n = 5), AI-Powered Robots (n = 4), Adaptive Learning Systems (n = 4), AI-Enhanced Extended reality (n = 3), and Conversational AI and Chatbot (n = 2), demonstrating potential benefits across cognitive, language, literacy, social, emotional, and fine motor domains. Most associations were found between AI and children’s language (n = 5) and literacy (n = 3). Author’s Conclusions: AI-based activities in early educational settings show preliminary promise, particularly for language and literacy, but the evidence base is limited and heterogeneous. More rigorous studies are needed to examine developmental outcomes, equity, safety, and sustained effects. Researchers should specifically address equity issues and potential adverse effects when conducting research in this area.
SN - 1891-1803
ER -