Galos, J. L., & Friedman, A. Z. C., & Jamosmos, E., & Allec, S. I., & Blinzler, B., & Grunenfelder, L., & Carberry, A. R. (2024, June), Teaching artificial intelligence and machine learning to materials engineering students through plastic 3D printing  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://strategy.asee.org/48057

\bibitem{asee_peer_48057}
  Galos, J. L., \& Friedman, A. Z. C., \& Jamosmos, E., \& Allec, S. I., \& Blinzler, B., \& Grunenfelder, L., \& Carberry, A. R. (2024, June), \emph{Teaching artificial intelligence and machine learning to materials engineering students through plastic 3D printing}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  https://strategy.asee.org/48057

Joel L Galos, Aaron Zachary Chandler Friedman, Ethan Jamosmos, Sarah Isabel Allec, Brina Blinzler, Lessa Grunenfelder, and Adam R Carberry.  "Teaching artificial intelligence and machine learning to materials engineering students through plastic 3D printing".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://strategy.asee.org/48057 Internet.  31 Jul, 2024

\bibitem{asee_peer_48057}
  Joel L Galos, Aaron Zachary Chandler Friedman, Ethan Jamosmos, Sarah Isabel Allec, Brina Blinzler, Lessa Grunenfelder, and Adam R Carberry.
  "Teaching artificial intelligence and machine learning to materials engineering students through plastic 3D printing".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://strategy.asee.org/48057 Internet.  31 Jul, 2024

@INPROCEEDINGS{asee_peer_48057
author = "Joel L Galos, Aaron Zachary Chandler Friedman, Ethan Jamosmos, Sarah Isabel Allec, Brina Blinzler, Lessa Grunenfelder, and Adam R Carberry"
title = "Teaching artificial intelligence and machine learning to materials engineering students through plastic 3D printing"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/48057}
}

TY  - CPAPER
AB  - Computational tools in conjunction with Artificial Intelligence (AI) and Machine Learning (ML) have the potential to play significant roles in the future of materials science and engineering (MS&E). Therefore, these concepts need to be introduced to students throughout existing MS&E curricula. However, there is currently a lack of datasets and tools that are appropriate for introducing the complex topics of AI and ML to engineering students with little to no knowledge of computer science. In this paper, we report on the background, development, and application of a new 3D printed plastic dataset and related active learning assignment. The active learning assignment was designed to introduce AI and ML concepts to students with little to no knowledge of computer science, computer programming (e.g., Matlab or Python) or algorithm development. This activity was performed on a relatively new “no-code” software platform (developed by Citrine Informatics) that uses AI and ML to solve real-world materials engineering problems. Some relevant existing online teaching resources were first reviewed with the aim of strengthening the aim and approach of the active learning assignment. An emphasis is placed on the importance of materials engineering domain knowledge and structured material data for the successful application of AI and ML in successfully solving materials engineering problems. At this stage, the purpose is to share our efforts and findings with educators, to get feedback and to inspire ideas for teaching AI and ML to engineering students without a programming background. Student perceptions of the class and its outcomes are also presented.
AU  - Joel L Galos
AU  - Aaron Zachary Chandler Friedman
AU  - Ethan Jamosmos
AU  - Sarah Isabel Allec
AU  - Brina Blinzler
AU  - Lessa Grunenfelder
AU  - Adam R Carberry
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Teaching artificial intelligence and machine learning to materials engineering students through plastic 3D printing
UR  - https://strategy.asee.org/48057
ER  -