An intelligent tutoring system that generates a natural language dialogue using dynamic multi-level planning

Woo, Chong Woo; Evens, Martha W.; Freedman, Reva; Glass, Michael; Shim, Leem Seop; Zhang, Yuemei; Zhou, Yujian; Michael, Joel

doi:10.1016/j.artmed.2005.10.004

« Previous Next »Artificial Intelligence in Medicine
Volume 38, Issue 1 , Pages 25-46, September 2006

An intelligent tutoring system that generates a natural language dialogue using dynamic multi-level planning

Chong Woo Woo
- School of Computer Science, Kookmin University, 861-1 Chongnung-Dong, Sungbuk-Ku, Seoul, Republic of Korea
Martha W. Evens
- Computer Science Department, Illinois Institute of Technology, Room 236, 10 West 31st Street, Chicago, IL 60616, USA
- Corresponding author. Tel.: +1 312 567 5153; fax: +1 312 567 5067.
Reva Freedman
- Northern Illinois University, De Kalb, IL 60115, USA
Michael Glass
- Valparaiso University, Valparaiso, IN 46383, USA
Leem Seop Shim
- HS Tech, Inc., 26500 Agoura Road, Suite #108, Calabasas, CA 91302, USA
Yuemei Zhang
- Wells Fargo - N9301-01J, 255 Second Avenue South, Minneapolis, MN 55479, USA
Yujian Zhou
- WebEx Communications, Inc., 3979 Freedom Circle, Santa Clara, CA 95054, USA
Joel Michael
- Department of Molecular Biophysics and Physiology, Rush Medical College, 1750 West Harrison, Chicago, IL 60612, USA

Received 16 February 2005; received in revised form 14 October 2005; accepted 21 October 2005.

Summary

Objective

The objective of this research was to build an intelligent tutoring system capable of carrying on a natural language dialogue with a student who is solving a problem in physiology. Previous experiments have shown that students need practice in qualitative causal reasoning to internalize new knowledge and to apply it effectively and that they learn by putting their ideas into words.

Methods

Analysis of a corpus of 75 hour-long tutoring sessions carried on in keyboard-to-keyboard style by two professors of physiology at Rush Medical College tutoring first-year medical students provided the rules used in tutoring strategies and tactics, parsing, and text generation. The system presents the student with a perturbation to the blood pressure, asks for qualitative predictions of the changes produced in seven important cardiovascular variables, and then launches a dialogue to correct any errors and to probe for possible misconceptions. The natural language understanding component uses a cascade of finite-state machines. The generation is based on lexical functional grammar.

Results

Results of experiments with pretests and posttests have shown that using the system for an hour produces significant learning gains and also that even this brief use improves the student's ability to solve problems more then reading textual material on the topic. Student surveys tell us that students like the system and feel that they learn from it. The system is now in regular use in the first-year physiology course at Rush Medical College.

Conclusion

We conclude that the CIRCSIM–Tutor system demonstrates that intelligent tutoring systems can implement effective natural language dialogue with current language technology.

Keywords: Intelligent tutoring system, Natural language dialogue, Instructional planning, Dynamic planning, Hierarchical planning, Reactive planning, Language understanding, Dialogue generation