Beyond Introductory AI
Deepak Kumar
Department of Mathematics & Computer Science
Bryn Mawr College Bryn Mawr, PA 19010

Curriculum Descant
From ACM Intelligence Magazine
Volume 10, Number 3, Fall 1999
ACM Press


Much of the discussion on the issue of teaching AI tends to be centered around the introductory course. Typically, the introductory AI course is offered in undergraduate programs at the junior/senior level. The underlying assumption is that such a course serves to capstone the learning experiences of a computer science student. In this installment, I would like to examine undergraduate courses that go beyond the standard introductory AI course.

It is important to recognize the diversity of computer science programs. Some undergraduate programs are a part of an established graduate program; however, many programs are stand-alone undergraduate programs. Even programs that offer graduate degrees restrict the number of undergraduate AI courses to more or less an introductory course, which is often cross-listed as a graduate-level course. This is true even in programs that are strong in AI research. Most AI courses that cover topics beyond the introductory course are designed for graduate students, but motivated undergraduate students can enroll in these courses. Occasionally, undergraduate students also undertake advanced work in AI research labs. Working together on a research project alongside graduate students is one of the most rewarding experiences for undergraduates.

For the majority of programs that offer only undergraduate level instruction in computer science, the possibility of offering even an introductory course in AI can be an issue. There may be limited resources, high demands of faculty on other areas of the curriculum, or the limited availability of faculty who are willing to teach AI. The school may not have any faculty whose research area is AI. Here, the definition of a core computer science curriculum plays an important role. If AI is prescribed by a standard curriculum (for instance the ACM/IEEE Curriculum 1991 lists several AI and AI related knowledge units), the likelihood of finding an AI course is greater.

Another parameter that can play an important part in determining the range of AI courses offered is the size of the program. Larger programs tend to have larger class enrollments. Sometimes, larger class sizes can limit the range of advanced courses offered. For instance, the use of LEGO-based robot labs (see
Curriculum Descant, SIGART Bulletin, Fall 1998) has been found to be more feasible in schools with smaller class sizes. Smaller class sizes also enable the creation of interdisciplinary AI courses that require active class participation. For example, I offer a course entitled, Biologically Inspired Computational Models of Learning, that is co-taught with a professor of neurobiology. Topics range from studying the fundamentals of neural networks, genetic algorithms, and evolutionary computation, with contributions and viewpoints from a biological perspective. The course inherently relies on smaller class sizes as it requires quite a bit of discussion and interaction.

Expanding the horizon to other disciplines on a campus can be a rewarding experience for both the faculty and students. Often, introductory AI courses can be found in psychology, information systems, linguistics, philosophy, and engineering programs. Other AI courses may be found in those programs that can extend the scope of any introductory AI course. In graduate school I took a course titled, Introduction to AI, in the psychology department. The course used the same text (Charniak & McDermott) as the the computer science offering; however, the syllabus and focus were entirely different. Whereas the computer science version concentrated on representation and algorithms, the psychology version focussed more on underlying cognitive studies and the feasibility of proposed models. Whether or not a computer science program offers an introductory AI course, similar offerings in other programs can help expand the options and increase the possibility of interdisciplinary collaborations.


Fall 1997
Inaugural Installment of the new column.
(Deepak Kumar)

Summer 1998
Teaching about Embedded Agents
Using small robots in AI Courses
(Deepak Kumar)

Fall 1998
Robot Competitions as Class Projects
A report of the 1998 AAAI Robot Competition and how robot competitions have been successfully incorporated in the curriculum at Swarthmore College and The University of Arkansas
Lisa Meeden & Doug Blank)

Winter 1998
Nilsson's New Synthesis
A review of Nils Nilsson's new AI textbook
(Deepak Kumar)

Spring 1999
Pedagogical Dimensions of Game Playing
The role of a game playing programming exercise in an AI course
(Deepak Kumar)

Summer 1999
A New Life for AI Artifacts
A call for the use of AI research software in AI courses
(Deepak Kumar)

Fall 1999
Beyond Introductory AI
The possibility of advanced AI courses in the undergraduate curriculum
(Deepak Kumar)

January 2000
The AI Education Repository
A look back at AAAI's Fall 1994 Symposium on Improving the Instruction of Introductory AI and the resulting educational repository
(Deepak Kumar)

Spring 2000
Interdisciplinary AI
A challenge to AI instructors for designing a truly interdisciplinary AI course
(Richard Wyatt)

Summer 2000
Teaching "New AI"
Authors of a new text (and a new take) on AI present their case
(Rolf Pfeifer)

Fall 2000
Ethical and Social Implications of AI: Stories and Plays
Descriptions of thought provoking stories and plays that raise ethical and social issues concerning the use of AI
(Richard Epstein)

January 2001
How much programming? What kind?
A discussion on the kinds of programming exercises in AI courses
(Deepak Kumar)

Spring 2001
Predisciplinary AI
A follow-up to Richard Wyatt's column (above) and a proposal for a freshman-level course on AI
(Deepak Kumar)

Spring 2001
Machine Learning for the Masses
Machine Learning comes of age in undergraduate AI courses
(Clare Congdon)

About Curriculum Descant
Curriculum Descant has been a regular column in ACM's Intelligence magazine (formerly published as ACM SIGART's Bulletin). The column is edited by Deepak Kumar. The column features short essays on any topic relating to the teaching of AI from any one willing to contribute. If you would like to contribute an essay, please contact Deepak Kumar.