A Scoping Study on AI Affordances in Early Childhood Education: Mapping the Global Landscape, Identifying Research Gaps, and Charting Future Research Directions

Abstract

Artificial intelligence (AI), manifested in the forms of technologies, systems, tools, and applications, has advanced rapidly, especially in recent years. It has permeated many aspects of human behavior and nearly all sectors of society, such as healthcare and education. In the context of early childhood education (ECE), AI has afforded valuable opportunities that directly and indirectly enhance children’s learning and development. While there are already two existing reviews of the literature on AI in ECE, they show either a lack of descriptive information concerning selected studies or inconsistencies between inclusion/exclusion criteria and selected studies, thereby raising concerns about their rigor. Representing a more methodologically rigorous effort and a significant contribution to the field of AI in ECE, this scoping study aimed to achieve three main goals: (1) “mapping” the global landscape of the current extent, range, and nature of relevant studies on the affordances of AI for use in ECE, (2) identifying potential research gaps, and (3) charting future research directions. Specifically, it addressed this overarching research question: What is the global landscape of the current state of knowledge concerning the affordances of AI for use in ECE? Specifically, the state of knowledge here refers to three aspects: (1) extent, (2) range, and (3) nature. First, regarding the extent aspect, the empirical knowledge was derived from 18 research articles in 11 countries and 16 peer-reviewed academic journals between 2005 and 2023, with 14 of these articles published in the past four years (2020–2023). Second, with respect to the range of study populations, it covered 15,081 children in early childhood (ages 2 to 8 years) across these 11 countries. Third, thematic analysis of these studies revealed four areas of AI affordances: (1) AI as tangible and intangible tools for interactive learning and information retrieval, (2) AI as technology for predicting/classifying children’s conditions, (3) AI as the object for learning by adapting to and personalizing children’s learning, and (4) AI as the subject for children's learning about it. Based on these findings, this scoping review identified three research gaps for future studies: (1) interviewing and/or surveying education stakeholders (parents, educators, policymakers) to explore the affordances of appropriate AI for use with, by, and for children bearing ethical considerations; (2) conducting group comparisons to investigate contextual factors contributing to the “AI divide” among children from different socioeconomic backgrounds; and (3) comparing sociocultural influences on AI use in ECE across cultures.