Seymour, Latisha
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Artificial Intelligence in Early Childhood Education: A Systematic Literature Review of Pedagogical Impacts and Ethical Challenges Rocha, Krysten; Dudley, Dean; Seymour, Latisha
Journal of Foundational Learning and Child Development Vol. 2 No. 01 (2026): Advancing Foundational Learning and Holistic Child Development in Early and Pr
Publisher : CV. INSPIRETECH GLOBAL INSIGHT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53905/ChildDev.v2i01.09

Abstract

Purpose of the study: This systematic literature review investigates the pedagogical impacts and ethical challenges associated with the integration of Artificial Intelligence (AI) technologies in Early Childhood Education (ECE) settings globally. The study aims to synthesize empirical evidence on AI-mediated learning outcomes, teacher-AI interaction dynamics, and the ethical dimensions of deploying intelligent systems with young children aged 0–8 years. Materials and methods: Following PRISMA 2020 guidelines, a comprehensive electronic search was conducted across seven major academic databases — Scopus, Web of Science, ERIC, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar — covering publications from January 2015 to December 2024. After rigorous screening against predefined inclusion/exclusion criteria, 63 peer-reviewed studies were retained for full analysis. Data were extracted using a standardized protocol and synthesized through thematic analysis combined with narrative synthesis. Results: The review identified five principal thematic clusters: (1) AI-enhanced personalized learning and adaptive instruction (n=18); (2) social-emotional development mediated by AI companions and robots (n=14); (3) language and literacy acquisition supported by conversational AI (n=13); (4) teacher professional development and pedagogical transformation (n=10); and (5) ethical, privacy, and algorithmic bias concerns (n=8). Findings indicate statistically significant improvements in phonological awareness, early numeracy, and engagement metrics across diverse cultural contexts. Concurrently, substantive concerns regarding data privacy, digital equity, and the risk of replacing human pedagogical relationships emerged consistently. Conclusions: AI technologies hold transformative potential for ECE when deployed under rigorous ethical frameworks that prioritize child welfare, developmental appropriateness, and equitable access. However, current evidence underscores significant research gaps regarding longitudinal outcomes, cultural transferability, and robust governance frameworks. Policy-makers, educators, and technology designers are urged to adopt co-design principles that centre the developmental rights of young children.