EAAI-13: The Fourth Symposium on Educational Advances in Artificial Intelligence

Bellevue, Washington, USA    (Collocated with AAAI-13)
July 15-16, 2013

Sponsored by the Association for the Advancement of Artificial Intelligence


The Fourth Symposium on Educational Advances in Artificial Intelligence (EAAI-13) was be held July 15-16th, 2013 in Bellevue Washington USA. The symposium is collocated with AAAI-13. EAAI-13 provides a venue for researchers and educators to discuss pedagogical issues and share resources related to teaching AI and using AI in education across a variety of curricular levels (K-12 through postgraduate training), with an emphasis on undergraduate and graduate teaching and learning.

The proceedings of the symposium are available here.


Program Schedule

Monday, July 15, 2013

Opening and Invited Talk

Coffee Break

Paper Session, Session Chair: Todd Neller

Model AI Assignments Session, Session Chair: Laura Brown

Panel: Educational Repositories for Teaching AI

Poster spotlights and open mic, Session Chair: Chris Brooks

5:30pm, Social Hour
Meet up and discuss topics with other EAAI attendees.

Tuesday, July 16, 2013

AAAI-13 Keynote (Ray Mooney)

IAAI-13 Invited Talk (Larry Birnbaum)

EAAI-13 Invited Talk

Teaching and Mentoring Session I

Teaching and Mentoring Session II

Senior Member Presentation

Big Ideas discussion

Invited Speakers

Dan Klein

University of California at Berkeley

Learning in teh Lab at Midnight: Experiences from Teaching AI at Berkeley and Online
Where does learning really happen? Only a little happens in lecture; most students learn much more working with friends in the lab at midnight. The modern student experience increasingly revolves around coursework, peer assistance, and asynchronous interactions -- not lectures, textbooks, and office hours. With these trends only increasing as enrollments rise and online channels emerge, how should we design our AI courses?

I'll talk about the best answers we've found so far for the Berkeley AI course. One key component of our approach is a set of thematically coherent, autograded projects that engage students and integrate with lectures in an ongoing way. More generally, I'll focus on several questions that have shaped our course, including: What should the role of a modern lecture be? What's the balance between cooperative learning and competition? When is an autograder more useful than a human TA? Why are students even taking AI in the first place? Finally, I'll talk about how technology that we originally developed for pedagogical purposes, such as rich autograding, has helped the course scale from tens to hundreds of students on campus and now to tens of thousands online.

Our experiences have resulted in a large number of re-usable materials, which we're always excited to share. I'll conclude with a discussion of how other instructors can take advantage of our lectures, interactive assignments, and autograded projects, which have already been used by over a hundred AI courses.

Paula Matuszek

Villanova University

Broader and Earlier Access to Machine Learning In a world of big data, everyone needs machine learning. But we don't know how to teach it to everyone. Machine learning has become increasingly important both in artificial intelligence and in computer science generally. This can be attributed primarily to three factors: the development of increasingly sophisticated algorithms, the availability of increasingly powerful hardware with increasingly large storage capacities, and the growth of the Internet. The last has provided an enormously rich pool of data to draw on, and at the same time a much greater need for tools to make some sense of all the data.

Machine learning approaches provide useful tools for a variety of problems in a many domains. Unfortunately, typical machine learning courses focus on the mathematical derivations for various algorithms and require an advanced level of mathematical sophistication. This puts them out of reach of many undergraduates, especially in domains other than computer science. Because machine learning has reached its current level of maturity, it should be feasible to teach a machine learning course that focuses on an intuitive understanding of some of these methods and their correct application, without requiring a deep understanding of their derivation.

At Villanova we have embarked on an NSF-funded project to develop a set of machine-learning modules. Our goal is to have students who can make intelligent and effective use of appropriate machine learning algorithms and techniques as tools. The modules will cover general concepts and specific applications, with examples drawn from a variety of domains. Each module will also include resources such as tools and data that are relevant to the topic. In this talk I will describe our pedagogical approach and share the state of the modules we have so far, as well as our plans. I hope also to stimulate discussion about our approach, examples, resources we should explore, and other attempts to teach machine learning to non-traditional audiences.


The AAAI-13/IAAI-13 technical program registration includes participation in EAAI-13 for invited participants and other interested individuals. If you are planning to attend AAAI-13/IAAI-13 register for the conference and indicate your intention to attend EAAI-13 on the registration form.

If you are attending the EAAI-13 sessions only, please contact AAAI at aaai13@aaai.org for further information.

Student Scholarships

Thanks to the generosity of the National Science Foundation under Grant Number IIS-1137085, we are able to offer a limited number of scholarships to undergraduate and graduate students at US universities to provide partial support for the costs involved in attending EAAI-13.

To apply for a scholarship, please send the following information to Laura E. Brown (lebrown@mtu.edu) by the scholarship application deadline. We will notify you of scholarship decisions prior to the AAAI/EAAI late registration deadline.

  1. Put "EAAI Scholarship" in the subject line of your email.
  2. Submit:

    • Your CV (PDF preferred).
    • A statement (up to one page) of what you expect to gain by attending EAAI. Please include a list of courses recently taught or TAed (and any relevant innovations developed in those courses, if applicable) and/or courses you anticipate teaching/TAing in the coming academic year. Finally, please list previous AAAI conferences attended, if any, and level of participation (author, presenter, poster, attendee, etc.).
    • An itemized list of your expected travel costs (registration, transportation, hotel, etc.).
    • Please also answer the following questions:

      • Are you a co-author of an accepted EAAI paper, poster, or Model AI Assignment?
      • Are you presenting at EAAI?
      • Have you previously attended EAAI? If so, in what capacity, author, presenter, poster, or attendee?

As a scholarship recipient, you will commit to:

  1. Attend EAAI and the EAAI Teaching and Mentoring Sessions on July 15th and 16th.
  2. Submit a post-conference summary of your EAAI experience and feedback on what was useful and what could be improved. We will use this in our post-conference report to the NSF as well as to consider changes for the next EAAI.

Questions? Contact Laura E. Brown (lebrown@mtu.edu).

Notes on minimizing attendance costs: Book your hotel as soon as possible and consider sharing a room to reduce costs. Also, information on discounted student accommodations at the conference hotel is available by writing to aaai13students@aaai.org; proof of full-time student status is required.

This material is based upon work supported by the National Science Foundation under Grant Number IIS-1337085.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Paper Submission

Paper submission deadline: February 6, 2013

EAAI-13 paper submissions should be in one of the following formats:

Author Registration
Authors must register at the EAAI-13 paper submission site. You will then receive a login and password via email, which will enable you to log on to submit an abstract and paper. In order to avoid a rush at the last minute, authors are encouraged to register well in advance of the paper submission deadline.

Abstract and Paper Submission
Complete details on submission requirements, including paper formatting guidelines, will be available at the AAAI EAAI-13 web site. Please pay careful attention to the submission instructions provided.

The EAAI-13 proceedings will be published by AAAI.

Model AI Assignments
Individuals interested in submitting work to the Model AI Assignments Session should consult the submission instructions at the Model AI Assignments web site.


Program co-Chairs

Organizing Committee

Program Committee

Eric Aaron, Wesleyan University
Stephanie August, Loyola Marymount University
Steve Bogaerts, Wittenberg University
Deb Burhans, Canisius College
Giuseppe Carenini, University of British Columbia
Zachary Dodds, Harvey Mudd College
Jason Eisner, John Hopkins University
Doug Fisher, Vanderbilt University
Susan Fox, Macalester College
Sven Koenig, University of Southern California
Simon Levy, Washington and Lee University
Percy Liang, Stanford University
Jim Marshall, Sarah Lawrence College
Fred Martin, University of Massachusetts Lowell
Vibhu Mittal, Root-1 Research
Dave Musicant, Carleton College
Keith O'Hara, Bard College
Jeff Pfaffmann, Lafayette College
David Poole, University of British Columbia
Vasile Rus, University of Memphis
Paul Ruvolo, Bryn Mawr College
Devika Subramanian, Rice University
Lisa Torrey, St. Lawrence University
Doug Turnbull, Ithaca College
Michael Wollowski, Rose-Hulman Institute of Technology


EAAI-13 will be collocated with AAAI-13 in Bellevue, Washington, USA.

More information about Bellevue, WA.


The following links are to various material on AAAI-13 and EAAI-13.