PopBots: Designing an Artificial Intelligence Curriculum for Early Childhood Education
Name
EAAI-WilliamsR.25.pdf
Description
Accepted version
Size
1014.59 KB
Format
Adobe PDF
Checksum (MD5)
c981e30cbf1eb88b468a2ca43c1f2be1
Author(s)
Williams, Randi
Park, Hae Won
Oh, Lauren
Breazeal, Cynthia
Date Issued
2019
Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Citation
Williams, Randi, Park, Hae Won, Oh, Lauren and Breazeal, Cynthia. 2019. "PopBots: Designing an Artificial Intelligence Curriculum for Early Childhood Education." Proceedings of the AAAI Conference on Artificial Intelligence, 33.
Version
Author's final manuscript
Abstract
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. PopBots is a hands-on toolkit and curriculum designed to help young children learn about artificial intelligence (AI) by building, programming, training, and interacting with a social robot. Today's children encounter AI in the forms of smart toys and computationally curated educational and entertainment content. However, children have not yet been empowered to understand or create with this technology. Existing computational thinking platforms have made ideas like sequencing and conditionals accessible to young learners. Going beyond this, we seek to make AI concepts accessible. We designed PopBots to address the specific learning needs of children ages four to seven by adapting constructionist ideas into an AI curriculum. This paper describes how we designed the curriculum and evaluated its effectiveness with 80 Pre-K and Kindergarten children. We found that the use of a social robot as a learning companion and programmable artifact was effective in helping young children grasp AI concepts. We also identified teaching approaches that had the greatest impact on student's learning. Based on these, we make recommendations for future modules and iterations for the PopBots platform.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1609/AAAI.V33I01.33019729
