AAAI 2024technical0 citations
Model AI Assignments 2024
Todd W. Neller, Pia Bideau, David Bierbach, Wolfgang Hönig, Nir Lipovetzky, Christian Muise, Lino Coria, Claire Wong
Abstract
The Model AI Assignments session seeks to gather and dis- seminate the best assignment designs of the Artificial In- telligence (AI) Education community. Recognizing that as- signments form the core of student learning experience, we here present abstracts of five AI assignments from the 2024 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment spec- ifications and supporting resources may be found at http://modelai.gettysburg.edu.
BibTeX
@article{Neller_Bideau_Bierbach_Hönig_Lipovetzky_Muise_Coria_Wong_Rosenthal_Lu_Gao_Zhang_2024, title={Model AI Assignments 2024}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30386}, DOI={10.1609/aaai.v38i21.30386}, abstractNote={The Model AI Assignments session seeks to gather and dis-
seminate the best assignment designs of the Artificial In-
telligence (AI) Education community. Recognizing that as-
signments form the core of student learning experience, we here present abstracts of five AI assignments from the 2024
session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment spec-
ifications and supporting resources may be found at http://modelai.gettysburg.edu.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Neller, Todd W. and Bideau, Pia and Bierbach, David and Hönig, Wolfgang and Lipovetzky, Nir and Muise, Christian and Coria, Lino and Wong, Claire and Rosenthal, Stephanie and Lu, Yu and Gao, Ming and Zhang, Jingjing}, year={2024}, month={Mar.}, pages={23370-23371} }