Intelligent tutoring systems (ITS) seek to mimic the learning improvement provided in a one on one tutor/student relationship. In order 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 to her (e.g. personality factors; preferred learning style; confidence/anxiety). We 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 we are applying these techniques to extend an ITS for training new helicopter pilots in the Army, where the subject matter experts are helicopter pilots.