This thesis presents an intelligent system for tutoring phonetic transcription in introductory linguistics courses. It compares and contrasts this system with previous intelligent tutoring systems and presents an implementation of the present system. The problems and solutions encountered in implementing the system are described.Among the contributions and innovations are the fact that this system guides the student through several attempts at transcribing a word with increasingly specific feedback, and the fact that the system is organized in such a way that an instructor can add, modify or delete data at any time with no assistance required from a programmer.A significant contribution of this system lies in the fact that although there is only one correct answer for any given item to be transcribed, the possibilities for the student's responses and hence for incorrect answers must be open-ended. The student's answer will be a string that may not have the same length as the correct answer, may contain few or none of the same symbols as the correct answer, and those that it does contain may be in a different order. The student's answer is intended to correspond to the correct answer, but is known not to be an exact match. Arbitrary strings representing the student's answers must thus be matched up with the pattern of the correct answer in such a way that the system can give the student meaningful comments that will aid the student in identifying errors. The usual pattern recognition program is designed to identify instances where a match succeeds. This tutor must identify instances where the match fails as well as how it fails. / Department of Computer Science