Model AI Assignments 2019


  • Todd W. Neller Gettysburg College
  • Raja Sooriamurthi Carnegie Mellon University
  • Michael Guerzhoy Princeton University
  • Lisa Zhang University of Toronto
  • Paul Talaga University of Indianapolis
  • Christopher Archibald Mississippi State University
  • Adam Summerville California State Polytechnic University
  • Joseph Osborn Pomona College
  • Cinjon Resnick New York University
  • Avital Oliver Google Brain
  • Surya Bhupatiraju DeepMind Technologies
  • Kumar Krishna Agrawal Google Brain
  • Nate Derbinsky Northeastern University
  • Elena Strange Northeastern University
  • Marion Neumann Washington University in St. Louis
  • Jonathan Chen Washington University in St. Louis
  • Zac Christensen Washington University in St. Louis
  • Michael Wollowski Rose-Hulman Institute of Technology
  • Oscar Youngquist Rose-Hulman Institute of Technology



The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //




How to Cite

Neller, T. W., Sooriamurthi, R., Guerzhoy, M., Zhang, L., Talaga, P., Archibald, C., Summerville, A., Osborn, J., Resnick, C., Oliver, A., Bhupatiraju, S., Agrawal, K. K., Derbinsky, N., Strange, E., Neumann, M., Chen, J., Christensen, Z., Wollowski, M., & Youngquist, O. (2019). Model AI Assignments 2019. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9751-9753.



EAAI Symposium: Model AI Assignments