Xu, G., & Zabner, D., & Cross, J. L., & Nadler, D. R., & Coxon, S. V., & Engelkenjohn, K. (2023, June), Board 157: Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning  (Work in Progress)  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42500

\bibitem{asee_peer_42500}
  Xu, G., \& Zabner, D., \& Cross, J. L., \& Nadler, D. R., \& Coxon, S. V., \& Engelkenjohn, K. (2023, June), \emph{Board 157: Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning  (Work in Progress)}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--42500

Geling Xu, David Zabner, Jennifer Light Cross, Dustin Ryan Nadler, Steven V. Coxon, and Karen Engelkenjohn.  "Board 157: Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning  (Work in Progress)".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/42500 Internet.  15 Dec, 2024

\bibitem{asee_peer_42500}
  Geling Xu, David Zabner, Jennifer Light Cross, Dustin Ryan Nadler, Steven V. Coxon, and Karen Engelkenjohn.
  "Board 157: Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning  (Work in Progress)".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/42500 Internet.  15 Dec, 2024

@INPROCEEDINGS{asee_peer_42500
author = "Geling Xu, David Zabner, Jennifer Light Cross, Dustin Ryan Nadler, Steven V. Coxon, and Karen Engelkenjohn"
title = "Board 157: Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning  (Work in Progress)"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/42500}
number = {10.18260/1-2--42500}
}

TY  - CPAPER
AB  - Artificial Intelligence (AI) enhanced systems are widely adopted in post-secondary education, however, tools and activities have only recently become accessible for teaching AI and machine learning (ML) concepts to K-12 students. Research on K-12 AI education has largely included student attitudes toward AI careers, AI ethics, and student use of various existing AI agents such as voice assistants; most of which has focused on high school and middle school. There is no consensus on which AI and Machine Learning concepts are grade-appropriate for elementary-aged students or how elementary students explore and make sense of AI and ML tools. AI is a rapidly evolving technology and as future decision-makers, children will need to be AI literate[1]. In this paper, we will present elementary students’ sense-making of simple machine-learning concepts. Through this project, we hope to generate a new model for introducing AI concepts to elementary students into school curricula and provide tangible, trainable representations of ML for students to explore in the physical world. In our first year, our focus has been on simpler machine learning algorithms. Our desire is to empower students to not only use AI tools but also to understand how they operate. We believe that appropriate activities can help late elementary-aged students develop foundational AI knowledge namely (1) how a robot senses the world, and (2) how a robot represents data for making decisions. Educational robotics programs have been repeatedly shown to result in positive learning impacts and increased interest[2]. In this pilot study, we leveraged the LEGO® Education SPIKE™ Prime for introducing ML concepts to upper elementary students. Through pilot testing in three one-week summer programs, we iteratively developed a limited display interface for supervised learning using the nearest neighbor algorithm. We collected videos to perform a qualitative evaluation. Based on analyzing student behavior and the process of students trained in robotics, we found some students show interest in exploring pre-trained ML models and training new models while building personally relevant robotic creations and developing solutions to engineering tasks. While students were interested in using the ML tools for complex tasks, they seemed to prefer to use block programming or manual motor controls where they felt it was practical.
AU  - Geling Xu
AU  - David Zabner
AU  - Jennifer Light Cross
AU  - Dustin Ryan Nadler
AU  - Steven V. Coxon
AU  - Karen Engelkenjohn
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Board 157: Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning  (Work in Progress)
UR  - https://peer.asee.org/42500
DO  - 10.18260/1-2--42500
ER  -