The Navy is shifting its training and education from traditional methods, such as on-site instruction, texts, and observing students uring drills, to computer-supported learning such as web-based nstruction and computer simulations in lieu of live drills. This transition presents the challenge of keeping the best parts of traditional methods of instruction while obtaining the advantages that computers afford. The challenge is more difficult because to maximize savings in manpower, money and time, computer-based learning must be able to teach, evaluate and give feedback to students without any instructor in the loop.

A valuable aspect of traditional training methods, in which computers currently fall short, is the 'mentor/student' relationship: an experienced person discussing a novice's performance with him or her. The mentor gives the student direct, personalized feedback in a setting where the student can ask questions and discuss issues. Most computer simulations are lacking in this type of interaction.

We propose that giving computers the ability to debrief and discuss a student's actions using natural language will more closely simulate this relationship and greatly improve the effectiveness of computer-based learning. To assess this hypothesis, we are utilizing natural language technology to (1) allow students to use a damage control trainer for surface ships by speaking with the simulation system, and (2) to support a subsequent spoken discussion with an intelligent tutoring system that provides an after action review of the student's performance. The combined system performs a mentoring function, helping students learn correct actions and avoid 'practicing mistakes'. We are studying the usefulness of this mentoring system for students under training in damage control, and will present results about differences in rate of learning with and without mentoring. An additional benefit of natural language interaction with the computer systems is that students train as they will actually perform on duty.