One of the shortcomings with traditional exams is that they are of fixed length; a learner must complete a long series of questions in order for the system to determine how well they know a subject. This characteristic can cause frustration for both novices and experts, who may know after a few questions that the subject matter is either bewildering or trivial. Aside from giving the learner greater control over exams - in that they are never forced to take a test - our primary strategy for tackling the problem of fixed-length exams is adaptive testing. Adaptive testing allows exams to be significantly shorter than traditional tests, without losing any predictive power about a learner's mastery of the content. The approach that is implemented in SWIFT is based on the work of [Welch & Frick, 1993]. The algorithm uses Bayes' theorem to estimate the probability that the learner is a master or non-master after each test question is answered. Furthermore, we modified the algorithm to allow a SME to include mandatory questions. Mandatory questions are presented if a Learner has mastered the test, if they do not master the test they are not presented. This graph represents the outcomes of a study that was completed comparing classroom instruction to four Intelligent Tutoring Systems. The study looked at (1) whether Tutors engender more effective and efficient learning in relation to traditional formats and (2) do they reduce the range of learning outcomes measures where a majority of individuals are elevated to high performance levels. The Tutors underwent systematic, controlled evaluations: a) Lisp tutor (Anderson Farrell & Sauers); b)Smithtown (Shute & Glaser, in press); Sherlock (Lesgold, Lajoie, Bunzo & Eggan); and d) The Pascal ITS (Bonar, Cunningham, Beatty & Weil).