International Journal of Academic Pedagogical Research (IJAPR)   Year: 2021 | Volume: 5 | Issue: 3 | Page No.: 124-137 Learning Machine Learning With Very Young Children: Who Is Teaching Whom? Ruslan Malikov, Nigora Abdiyeva, Boboxolova Marjona, Jurabek Abdiyev, Kuchiboyeva Nargila Abstract: While artificial intelligence and machine learning-based technology is becoming a commonplace feature of people's everyday lives, so far few theoretical or empirical studies have focused on investigating it in K-12 education. Drawing on the sociocultural theory of learning and participation, this case study explored how six very young children taught and explored Google's Teachable Machine in non-school settings. Through fine-grained analysis of video recordings and interviews with the children, the article illustrates the content and the process of teaching where 3-9 year old children were producing machine learning datasets and models as well as observing, exploring, and explaining their own interaction with machine learning systems. The results illustrate the quick-paced and embodied nature of the child- computer interaction that also supported children to reason about the relationship between their own bodily expressions and the output of an interactive ML-based tool. The article concludes with discussions on the emergent process of teaching and learning as well as on ways of promoting children's participation and sense of agency in the age of machine learning.