Integrating artificial intelligence in early childhood education: a systematic review of empirical studies
Interactive Learning Environments, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Derleme
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/10494820.2026.2734311
- Dergi Adı: Interactive Learning Environments
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, EBSCO Education Source, Education Abstracts, Educational research abstracts (ERA), ERIC (Education Resources Information Center), INSPEC, Psycinfo, EBSCO Communication Source, Academic Search Ultimate (EBSCO), Social Science Premium Collection (ProQuest), Communication Source (EBSCO), Education Collection (ProQuest), Education Source Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: Artificial intelligence, digital pedagogies, early childhood education, human–AI interaction, preschool learning
- Eskişehir Osmangazi Üniversitesi Adresli: Evet
Özet
Artificial intelligence (AI) is increasingly transforming early childhood education (ECE), yet empirical evidence on how it is implemented in authentic preschool settings remains limited. This study presents a systematic review of empirical studies published between 2022 and 2025 that investigate the use of AI technologies in ECE. Following PRISMA guidelines, seven peer-reviewed empirical studies were identified through searches in Scopus, Web of Science, and EBSCO. The studies were analyzed in terms of their pedagogical aims, AI technologies employed, research designs, participants, data collection instruments, implementation challenges, and recommendations for future research. The findings indicate that AI technologies were primarily used to support AI literacy, computational thinking, early STEM-related learning experiences, and interactive learning activities. These experiences were implemented through play-based, story-based, embodied, and machine learning-supported educational approaches. AI-supported interventions were associated with improvements in computational thinking, sequencing, self-regulation, theory of mind, number recognition, and children’s engagement in learning activities. Despite these promising outcomes, studies reported limitations related to small sample sizes, single-site implementations, short intervention periods, and technological infrastructure constraints. By synthesizing emerging empirical evidence, this review provides an overview of current trends and research gaps and offers a foundation for future research on developmentally appropriate and pedagogically meaningful AI integration in ECE.