Exploring the Dynamics of Scaffolding in K-12 ML/AI Education: Insights from a Machine Learning Workshop

Abstract

This study investigates the rarely-explored scaf-folding processes in teaching artificial intelligence (AI), more specifically machine learning (ML), to K-12 students using educational technology. Focusing on a ML workshop within a children's science camp, we observed 7-12 year-olds interacting with image classifiers using their own drawings, guided by an experienced computing teacher. Our analysis highlights the importance of the teacher's role in a technology-rich environment in using diagnostic questions to reveal and address students' misconceptions, aligning with the concept of contingent support. By linking theoretical concepts to practical activities, the teacher helped shift the focus from surface features to deeper processes, promoting advanced reasoning. The paper discusses a distributed scaffolding system combining teacher guidance, technological affordances, and peer interaction, crucial for making complex concepts accessible to young learners. These insights are important for educators and technology developers in enhancing K-12 ML/AI education.