Model AI Assignments 2024

Authors

  • Todd W. Neller Gettysburg College
  • Pia Bideau Technical University of Berlin
  • David Bierbach Humboldt University of Berlin
  • Wolfgang Hönig Technical University of Berlin
  • Nir Lipovetzky The University of Melbourne
  • Christian Muise Queen’s University
  • Lino Coria Northeastern University
  • Claire Wong Carnegie Mellon University
  • Stephanie Rosenthal Carnegie Mellon University
  • Yu Lu Beijing Normal University
  • Ming Gao Shanghai Normal University
  • Jingjing Zhang Beijing Normal University

DOI:

https://doi.org/10.1609/aaai.v38i21.30386

Keywords:

Model AI Assignments

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.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Neller, T. W., Bideau, P., Bierbach, D., Hönig, W., Lipovetzky, N., Muise, C., Coria, L., Wong, C., Rosenthal, S., Lu, Y., Gao, M., & Zhang, J. (2024). Model AI Assignments 2024. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23370-23371. https://doi.org/10.1609/aaai.v38i21.30386

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

EAAI Symposium: Model AI Assignment Abstracts