Balancing Artificial Intelligence and Human Insight in Early Childhood Education: Implications for Child Development

Abstract

Artificial intelligence technology is increasingly integrated into education, offering potential benefits for personalized feedback and data-driven insights. However, its effectiveness in early childhood education, particularly in terms of teachers’ perceptions and experiences, remains underexplored. This study aimed to evaluate the effectiveness of AI-driven tools in early childhood education, focusing on learning outcomes, usability, feedback quality, and teacher workload in preschool and kindergarten settings. A mixed-methods design was used, comprising a survey of 40 teachers and semi-structured interviews with 10 participants. Quantitative data were analyzed through descriptive statistics and ANOVA, while qualitative data were analyzed using thematic analysis. 80% of teachers believed AI tools enhance learning outcomes, with experienced teachers more favorable toward AI feedback. Challenges included AI's inability to interpret social cues, emphasizing the need for human interaction in early learning. AI tools should complement human teaching, with a focus on teacher training and balancing AI with human interaction in early education.