Transforming Global Early Childhood Education Through Artificial Intelligence: Addressing Teacher Shortages, Inequality, and Quality Gaps in Early Learning Systems
DOI:
https://doi.org/10.63539/isrn.2026027Keywords:
Artificial Intelligence, Early Childhood Education, Teacher Shortages, Educational Inequality, Learning Quality.Abstract
This study examines the potential of artificial intelligence (AI) to support early childhood education (ECE) in addressing workforce pressures, inequality, and quality-related challenges, while identifying the organisational, pedagogical, and ethical conditions required for responsible AI use. An interpretivist qualitative design was used with 22 participants (8 head teachers and 14 early childhood teachers) from ECE institutions in Sydney, Australia, who took part in semi-structured face-to-face interviews. Six interconnected themes emerged from inductive thematic analysis: workforce and operational challenges; AI awareness and readiness; AI as teacher support, not replacement; AI and equity; AI and learning quality; and responsible AI adoption. Participants considered how AI could support planning, documentation, translation, administration, and personalised learning, freeing up time for meaningful teacher–child interaction. They stressed, however, that AI cannot address structural workforce shortages and cannot take the place of teachers’ relational, professional, and safeguarding roles. The study’s contribution lies in bringing together workforce, equity, learn-ing quality, and responsible AI adoption within a single ECE-focused empirical framework, and in highlighting the necessity of human-centred implementation. Longitudinal and mixed-methods studies in various settings are needed to inves-tigate the impact of AI on teacher workload, child outcomes, equity, and policy implementation.
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