Intelligent tutoring systems (ITS) seek to mimic the learning improvement provided in a one-on-one tutor/student relationship. To effectively teach to a student, the ITS must adapt to the student's current understanding. Many ITSs judge a student's knowledge by current and historic performance in a subject area. From this information, an ITS can determine a number of facts about the student relevant to tutoring. This current/past performance view of tutoring ignores many aspects particular to a student, which would be useful in teaching (e.g., personality factors, preferred learning style, confidence/anxiety). The authors view an adaptive instructional system (AIS) as an extension to an ITS that also takes into account these types of individual trait and state differences. The adaptations used by the AIS have been collected from both relevant literature and interviews with domain experts. Currently, the authors are applying these techniques to extend an ITS for training new helicopter pilots in the Army, where the subject matter experts are helicopter pilots. In current initial entry rotary wing (IERW) training, an instructor pilot (IP) is assigned two students. These two students train in the helicopter with the same IP until they complete the current training phase and check-ride. Researchers have examined replacing some of the actual flight training with simulation instruction for beginning pilots. The main drawback of this is that an IP is required for all simulator training to ensure that students don't acquire any bad habits. The Intelligent Flight Trainer (IFT) takes the simulator's role in training a step farther. Rather than have IPs train students in the simulator, the IFT takes on the tasks of an instructor pilot. This means that in addition to simulating helicopter flight, the IFT must also perform as an instructor pilot. The IFT consists of a helicopter flight simulator and an intelligent tutoring system (ITS) merged into a single system.