Predicting Students' Interest from Small Group Conversational Characteristics: Insights from an AI Literacy Education with High School Students

Abstract

Recent years have seen developments in AI instructional practices for K-12 students. In literature, students' interest in AI is shown to correlate with gaining AI knowledge; however, little is known about how AI interest manifests in classroom discourses during AI literacy lessons. This study examined students' participation in an integrated AI curriculum delivered to a cognitive science class in a high school in the southern US. Students worked in small groups and built a supervised machine learning model to recognize kids' drawings at different stages of artistic development. Our analysis showed that semantic features extracted from students' small group conversations significantly predicted their interest in learning AI. However, we found no significant relationship between students' social construction of knowledge and their interests. This study sheds light on the relationship between the learning process and interest; when further developed, this analysis may be developed into a classroom activity analytics tool that may provide real-time feedback to teachers engaged in AI literacy education to enhance teaching effectiveness in this nascent content area.