The ultimate goal of the intelligent tutoring system is to mimic human tutors by adapting instruction to an individual student’s performance, strengths and weaknesses, and other attributes. Normally, intelligent tutoring systems monitor a student’s decisions and actions in simulated states, evaluate them, provide feedback, and infer the student’s knowledge and skills and ability to apply them based on the student’s performance in different scenarios.

Moreover, intelligent tutoring systems frequently devise instructional plans for students. They are based on Artificial Intelligence (AI) technology. Instruction is adapted to individuals from the Student Model, not directly from the student’s actions. Basically, intelligent tutoring systems are often interfaced to interactive multimedia instruction (IMI).

In an intelligent tutoring system, the student interacts primarily with a training simulation. This interaction is monitored by the intelligent tutoring system which receives from the simulation the student’s actions and decisions as well as the condition in which the student is making those decisions.

This allows the intelligent tutoring system to assess those decisions and actions, provide feedback for the student and formulate a plan of remediation. The remediation plan often involves selection of scenarios to test the effects of remediation, to practice areas the student is weakest in, and to illustrate points in which the student would need to improve in order to combat deficiencies it perceives the student has.