Pathways to Learning Computer Science: Exploring High School Students' Learning of AI-Powered Educational Robotics

• In Event: AERA Poster Session Sunday 11:45 am
  In Poster Session: Making Learning Work: Design and Technology

Abstract

AI-powered robotics are increasingly prominent in K–12 settings. While prior research highlights their positive impact on students' computer science (CS) attitudes and skills, less is known about how students learn robotics itself. Drawing on epistemic pluralism, we examined diverse ways students engage with and learn AI-powered robotics while designing solutions to a recycling problem. Fifteen high school students participated in a semester-long, project-based robotics and AI curriculum. A Wilcoxon Signed-Rank test showed significant gains in self-reported robotics knowledge (Z = -2.63, p < .01). Thematic analysis of field notes, interviews, and artifacts revealed that students learned through (1) real-world practices, (2) designing costumes, and (3) playful, creative expression. These findings contribute to research on alternative approaches to CS learning.

Authors

• Gözde McLaughlin, Kent State University

• Elena Novak, Kent State University

• Sima Ahmadi, Kent State University

• Jian Li, Kent State University

• Yibei Guo, Kent State University

• Rui Liu, Kent State University

• Lisa A. Borgerding, Kent State University