An intelligent tutoring system is one that modifies its interaction with the person being tutored bases on the tutee's performance. Just as an experienced teacher will adopt his information presentation to the characteristics of the learner so an intelligent tutoring system with adjust the rate of presentation to the learners prior knowledge, level of interest, and general intelligence.
THE NEED FOR INTELLIGENT TUTORING SYSTEMS (ITS'S)
While there is no restriction of intelligent tutoring systems to topics of military importance, probably the greatest stimulus to their development comes from the armed forces. Each year several hundred thousand individuals begin advanced individual training as members of the United States' Armed Forces. The majority of these will serve out their initial enlistment period then go on to alternative civilian employment. As a result the will be a tremendous need for basic technical training, probably a greater need than can be met with the available teaching manpower at any time in the near future.
Though traditional instruction in engineering, physics, and chemistry have focused on students' problem solving ability, students can circumvent the process by memorizing a limited set of problem templates. Indeed, the need to confirm that students are actually developing their understanding of the material is always a background consideration.
Several computer programs have been developed in recent years as intelligent tutoring systems. Among the major sponsors of research in this area are the National Science Foundation, the Institute of Education Sciences and the Office of Naval Research. These researchers are slowly converging upon a general framework for intelligent tutoring systems.
The domain model involves the structure of the knowledge to be developed in the student. It normally contains the knowledge that experts bring to the subject along with an awareness of pitfalls and popular misconceptions.
The learner model consists of the succession of psychological states that the learner is expected to go through in the course of learning. It can be viewed as an overlay of the domain model, which will change over the course of learning.
The pedagogical model takes the domain and learner models as input and selects the next move that the tutor will take. In mixed initiative systems, learners may take actions, or ask for help as well.
The tutor – user interface records and interprets the learner's responses obtained through various inputs: mouse clicks. typed-in answers, eye tracking, and possibly postural data and keeps a record as the tutoring progresses.
Here are several examples of learner modeling, used in contemporary ITS's:
Knowledge tracing: If the knowledge to be conveyed is contained in a set of production rules then skillometer can be used to visually display the students progress. Step by step knowledge tracing is incorporated in a number of tutoring programs developed in the Pittsburgh Science of Learning Center.
Constraint bases modeling: A relevant satisfied state constraint corresponds to an aspect of the problem solution. Learner modeling is tracked by which constraints are followed by students in solving problems. Successful constraint based tutors include the structured query language (SQL) tutor.
Knowledge space models underlies the very successful Assessment and Learning in Knowledge Spaces (ALEKS) mathematics tutor now widely used on college campuses to deal with students have not studied mathematics in recent semesters. The knowledge space is a large number of possible knowledge states of each mathematical field. As the topics are reviewed the student builds a coherent picture if the field as a whole.
Expectation and misconception tailored dialog. These are particularly important in fields like physics where terms from common usage, like force and energy have specific restricted meanings which students must understand before progress is possible.
Donald Franceschetti, PhD
Mandl, H., and A. Lesgold, eds. Learning Issues for Intelligent Tutoring Systems. New York: Springer, 1988. Print.
Stein, N. L., and S.W. Raudenbush, eds. Developmental Cognitive Science Goes to School. New York: Routledge, 2011. Print
Sottilare, R., Graesser, A., Hu, X., and Holden, H. (Eds.). (2013). Design Recommendations for Intelligent Tutoring Systems: Volume 1 - Learner Modeling. Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9893923-0-3. Available at: https://gifttutoring.org/documents/42