AN AI-DRIVEN TEACHING PROTOTYPE FOR EARLY CHILDHOOD EDUCATION: A DEVELOPMENTAL APPROACH TO EDUCATIONAL MANAGEMENT - Oshinowo, Oluwakemi Racheal; Agoi, Moses Adeolu; Sain, Zohaib Hassan; Sain, Shazadi Hina; Aziz, Aulia Luqman (Acitya Wisesa: Journal of Multidisciplinary Research, 2025)
Early childhood represents a critical period for cognitive, social, and emotional development, where foundational skills in literacy, numeracy, attention, and social interaction are established. High-quality early childhood education (ECE), as underscored by research such as that of Roslan et al. (2022), is pivotal for preparing children for formal schooling and fostering long-term academic and social success. This study investigates the integration of an AI-driven teaching prototype, incorporating interactive, adaptive storytelling agents and gamified learning platforms, within the ECE environment. A mixed-methods approach was employed, involving 80 children and 16 educators. The research instruments demonstrated high reliability, with a Cronbach's α of 0.782. Quantitative findings revealed significant improvements in several domains: reading skills (22%), mathematical understanding (17%), attention span (19%), and teacher-reported instructional effectiveness (24%). Qualitatively, the AI prototype was found to enhance student engagement, acceptance, and personalised learning experiences, thereby enabling educators to dedicate more time to providing targeted social-emotional support and individualized instruction. However, the study also identified significant challenges, including disparities in technology access, the potential for over-reliance on AI tools, and insufficient teacher training for effective technology integration. These findings emphasize the necessity for a carefully managed, human-centric implementation strategy. We conclude that while AI-driven tools hold transformative potential for ECE by augmenting pedagogical capabilities and personalizing education, their efficacy is contingent upon equitable access, ethical application, and sustained teacher professional development. Recommendations are offered for policymakers, educational administrators, and teachers to guide the sustainable integration of AI in early learning environments.