Workshop on Tutorial Dialogue Systems
Sunday, May 20, 2001
San Antonio, TX
 
 
 

To be held during AI-ED 2001,
the 10th International Conference on Artificial Intelligence in Education.





General Topic
Special Focus
Intended Audience
Workshop Format
Outcome
Organizing Committee
Program
 

Last updated: 5/14/2001

GENERAL TOPIC

Human one-on-one tutoring is the most effective form of instruction. Although the best intelligent tutoring systems have been shown to be more effective than classroom instruction, they are only half as effective as human tutors. Much of the success of human tutors seems to hinge on their ability to engage students in dialogue. It is therefore an interesting and promising hypothesis that intelligent tutoring systems will be more effective if they engage students in dialogue or support effective dialogue between learners. This raises a number of broad research questions: The workshop will deal with all issues related to these broad questions, including (but not limited to) empirical studies of tutorial discourse, the use of natural language understanding and generation technologies, the representation of pedagogical strategies and knowledge, the use of dialogue and text planning, and studies of the effectiveness of tutorial dialogue systems.

Although the field of AI & Education has a long-standing interest in these questions, they are more in the foreground now than before, due to advances in technologies such as natural language processing, knowledge representation, virtual reality, and multi-modal interfaces. The recent AAAI Fall Symposium on the topic of tutorial dialog systems reflects the surge of interest in both the AI & Education and computational linguistics communities.
 

SPECIAL FOCUS: UNDERSTANDING THE TRADE-OFFS BETWEEN ARCHITECTURAL COMPLEXITY AND PEDAGOGICAL EFFECTIVENESS

The workshop will focus on understanding the trade-offs between the complexity of a tutorial dialogue system and its pedagogical effectiveness. Tutorial dialogue systems tend to be complex. They contain not just the components found traditionally in intelligent tutoring systems, but many other components as well, such as a parser, semantic analyzer, dialogue planner, text planner, natural language realization component, and a virtual reality module. In the face of this complexity, it is good to ask, where is the biggest bang for the buck? What level of architectural complexity gives the greatest pedagogical pay-off? Is adding complexity always a good thing? What minimum level of complexity is required? Complexity can be measured, among other ways, in terms of development effort or the elaborateness of the architecture. Effectiveness on the other hand can be measured as the range of dialogue phenomena that the system supports, the generality of the approach, the ease of maintenance, or, ultimately, the students' learning gains.

It may well be that the trade-offs differ depending on the application domain, the overall pedagogical approach of the system, and the purpose for which it uses natural language processing. Nonetheless, it is likely that some common trade-offs, and ways of dealing with them, can be found that hold across domains.

INTENDED AUDIENCE

The intended audience for this workshop are, in the first place, researchers working on the development of educational systems that support dialog. Researchers working on issues related to the analysis or modeling of tutorial dialogue may also find this workshop of interest.
 

WORKSHOP FORMAT

The workshop will consist of paper presentations, discussion sessions, and possibly a demo session.

The workshop will be organized thematically. Approximately three themes will be selected based on the paper submissions. Possible themes include (but are not limited to):

There will be one session per theme, led by a person knowledgeable in that area. During each session, there will be a small number of paper presentations followed by a discussion and a brief wrap-up by the session leader. The discussion will focus on comparing and contrasting the approaches and on exploring whether and when certain positions along the various tradeoffs are more promising than others.

The presenters will be asked to relate their papers to the session theme as part of their presentation. Further, presenters will be asked to study the other papers in their session, so that during their presentation, they can compare and contrast their approach to others, as a way to kick off the discussion. We plan to make available the accepted papers to all workshop participants beforehand in order to ensure an informed discussion.
 

OUTCOME

All accepted papers and abstracts will be made available on the workshop web site.
 

FOR FURTHER INFORMATION

For any inquiries related to this workshop, please contact Vincent Aleven (aleven@cs.cmu.edu).

ORGANIZING COMMITTEE

Vincent Aleven
Human Computer-Interaction Institute
Carnegie Mellon University
aleven@cs.cmu.edu

Mark Core
Human Communication Research Centre
University of Edinburgh
markc@cogsci.ed.ac.uk

Jerome Lehuen
Equipe Langue et Dialogue
Laboratoire d'Informatique
Universite du Maine
Jerome.Lehuen@lium.univ-lemans.fr

Rachel Pilkington
Computer Based Learning Unit,
The University of Leeds,
R.M.Pilkington@cbl.leeds.ac.uk

Carolyn Penstein Rose
Learning Research and Development Center
University of Pittsburgh
rosecp+@pitt.edu

Florence M. Reeder
George Mason University /
The MITRE Corporation
freeder@mitre.org

Jeff Rickel
USC Information Sciences Institute
rickel@ISI.EDU

Peter Wiemer-Hastings
Human Communications Research Centre
University of Edinburgh
peterwh@cogsci.ed.ac.uk

Beverly Park Woolf
Department of Computer Science
University of Massachusetts
bev@cs.umass.edu