Optimising Early Learning via Generative AI: Theory and Implications for A Digital Generation

Abstract

Engulfing early childhood education, the digital age sparks questions regarding intelligent pedagogical approaches. Central to this discourse, this study unravels the theoretical implications of Generative Artificial Intelligence (GAI) application within early learning contexts. Preceding investigations have alluded to GAI's potential in personalising learning; however, comprehensive theoretical exploration remains in its infancy. Hence, this work strives to bridge this gap, shedding light on an intriguing research avenue. It emphasises GAI's probable impact on cognitive development, socio-emotional growth, ethical considerations, and risk mitigation among learners aged zero to eight. The theoretical analysis conducted herein benefits from Krashen's Second Language Acquisition Theory and Vygotsky's Social Development Theory, integrating their principles into the GAI context. Preliminary findings suggest GAI could yield an innovative learning model, personalised, adaptive, and contextually responsive. This implies an enhanced early childhood education landscape, fostering a healthier, intelligent digital generation. The investigation's crux rests on its novelty: pioneering a unique intersection of GAI and early childhood education theory. Proffering an enriched theoretical perspective generates dialogue surrounding digital education's future and its ramifications for contemporary pedagogy. As this study is seminal, it beckons subsequent empirical research to validate the posited theoretical premises.