In Teaching Integrated AI Through Interdisciplinary Project-Driven Courses, Eric Eaton presents his
work on an advanced robotics course that takes an
interdisciplinary project-driven approach toward
teaching AI. Interdisciplinary courses and project-based learning are on the rise at the K– 12 level
(Zubrzyck 2016) and a recent survey of AI practitioners (Wollowski et al. 2016) found that 41 percent of
the respondents suggest systems engineering as a
learning outcome. Eaton’s course fits this mold by
providing for challenging problems that require the
integration of multiple AI methods.
The article Ethical Considerations in Artificial
Intelligence Courses by Emanuelle Burton, Judy
Goldsmith, Sven Koenig, Benjamin Kuipers, Nicholas
Mattei, and Toby Walsh is concerned with providing
students learning opportunities about ethical theories — something that recently came to the forefront
of public attention though remarks by high-profile
entrepreneurs and prominent AI researchers through
efforts such as the Future of Life Institute,
1 the Allen
Institute for Artificial Intelligence,
2 and the recent
Partnership on AI.
3 The authors are interested in
challenging students to probe their own ethical perspectives and make them explicit. In the context of
an AI course, students investigate how their ethical
theories may inform the design of intelligent systems. The authors hold that as educators, we have a
responsibility to train students to recognize the larger ethical issues and responsibilities that their work as
technologists may encounter.
Keeping It Real: Using Real-World Problems to
Teach AI to Diverse Audiences by Nicole Sintov,
Debarun Kar, Thanh Nguyen, Fei Fang, Kevin Hoffman, Arnaud Lyet, and Milind Tambe is an exemplar
of using projects from the real world to introduce AI
to diverse audiences inside and outside of academe.
This article is in keeping with a recent survey of current practice and teaching of AI (Wollowski et al.
2016), which found a desire for exposing students to
solving real-world problems. This article too provides
a fine example of how to broaden AI expertise, a goal
stated in the Artifcial Intelligence and Life in 2030
report (Stone et al. 2016).
Finally, in Using AI to Teach AI: Lessons from an
Online AI Class, Ashok Goel and David Joyner describe
details of the very successful online version of their
course on knowledge-based AI. A key challenge that
they address is how to keep students engaged in online
courses. Goel and Joyner explain how they were able to
rise to this challenge. They supplement traditional
forms of communication with an innovative use of
intelligent tutoring agents and video lessons. The
online version of their course facilitates a unique and
promising way in which students develop a learning
community. An additional benefit is that their online
version is effective in extending the AI classroom experience to nontraditional students. This article is also an
exemplar of how to broaden AI expertise.
Hearst, M. 1994. Preface: Improving Instruction of Intro-
ductory AI. In Improving Instruction of Introductory Artifcial
Intelligence: Papers from the AAAI Fall Symposium. AAAI Tech-
nical Report FS-94-05. Menlo Park, CA: AAAI Press.
Stone, P.; Brooks, R.; Brynjolfsson, E.; Calo, R.; Etzioni, O.;
Hager, G.; Hirschberg, J.; Kalyanakrishnan, S.; Kamar, E.;
Kraus, S.; Leyton-Brown, K.; Parkes, D.; Press, W.; Saxenian,
A.; Shah, J.; Tambe, M.; and Teller, A. 2016. Artifcial Intelligence and Life in 2030. One Hundred Year Study on Artificial
Intelligence. Report of the 2015 One Hundred Year Study
Panel. Stanford, CA: Stanford University.
Wollowski, M.; Selkowitz, R.; Brown, L. E.; Goel, A.; Luger,
G.; Marshall, J.; Neel, A.; Neller, T.; and Norvig, P. 2016. A
Survey of Current Practice and Teaching of AI. In
Proceedings of the Thirtieth AAAI Conference on Artifcial Intelligence.
Palo Alto, CA: AAAI Press.
Zubrzyck, J. (2016). As Project-Based Learning Gains in Popularity, Experts Offer Caution. Education Week Blogs,
2016. Bethesda, MS: Editorial Projects in Education.
based_learning_gain.html) downloaded Dec. 15, 2016.
Michael Wollowski is an associate professor in the Computer Science Department at Rose-Hulman Institute of Technology. He obtained his Ph.D. from Indiana University,
developing a complete and sound diagrammatic logic for
planning in the blocks world. Wollowski’s research interests
focus on AI education, reasoning in natural language processing, and the Internet of Things.
Todd W. Neller is a professor of computer science at Gettysburg College. A Cornell University Merrill presidential
scholar, he received a B.S. in computer science with distinction in 1993. In 2000, he received his Ph.D. with distinction
in teaching at Stanford University, where he was awarded a
Stanford University Lieberman Fellowship, and the George
E. Forsythe Memorial Award for excellence in teaching. A
game enthusiast, Neller has in recent years enjoyed pursuing game AI challenges, computing optimal play for jeopardy dice games and bluffing dice games, creating new reasoning algorithms, analyzing optimal risk attack and
defense policies, and designing logic mazes.
Jim Boerkoel is an assistant professor in the Computer Science Department at Harvey Mudd College where he leads the
Human Experience and Agent Teamwork Lab. Boerkoel
received his B.S. from Hope College (2006), and his M.S.
(2008) and Ph.D. (2012) in computer science and engineering
from the University of Michigan under the supervision of Ed
Durfee. Prior to joining HMC, Boerkoel worked as a postdoctoral associate with Julie Shah of the Interactive Robotics
Group at the Massachusetts Institute of Technology. In 2017,
Boerkoel was recognized with an NSF CAREER award for his
project Robust and Reliable Multiagent Scheduling under
Uncertainty. His research interests include automated planning and scheduling, multirobot coordination, human-robot
interaction, and AI education.