Keeping it Real: Using Real-World Problems to Teach AI to Diverse Audiences


  • Nicole Sintov The Ohio State University
  • Debarun Kar University of Southern California
  • Thanh Nguyen University of Michigan
  • Fei Fang Carnegie Mellon University
  • Kevin Hoffman Aspire Public Schools
  • Arnaud Lyet World Wildlife Fund
  • Milind Tambe University of Southern California



In recent years, AI-based applications have increasingly been used in real-world domains. For example, game theory-based decision aids have been successfully deployed in various security settings to protect ports, airports, and wildlife. This article describes our unique problem-to-project educational approach that used games rooted in real-world issues to teach AI concepts to diverse audiences. Specifically, our educational program began by presenting real-world security issues, and progressively introduced complex AI concepts using lectures, interactive exercises, and ultimately hands-on games to promote learning. We describe our experience in applying this approach to several audiences, including students of an urban public high school, university undergraduates, and security domain experts who protect wildlife. We evaluated our approach based on results from the games and participant surveys.




How to Cite

Sintov, N., Kar, D., Nguyen, T., Fang, F., Hoffman, K., Lyet, A., & Tambe, M. (2017). Keeping it Real: Using Real-World Problems to Teach AI to Diverse Audiences. AI Magazine, 38(2), 35-47.