Many computer-assisted language learning systems specifically designed to be used in the curriculum and which exploit AI techniques have neither a learner model nor consequently any deep error analysis. Evidence from applied linguistics shows that learners have their own system of rules for the production of a foreign language. We believe the central issue is to determine the appropriate level of description of these rules and uncover the strategies used by the learners in particular situations. This information represents the major part of the learner model. We review error analysis in second language learning and tutoring systems related to this perspective. We introduce a new structure, called an "applicable rule", that can be used to help diagnose and to represent a learner's performance. We propose a design for the architecture of a system for computer diagnoses of learners' grammatical performances in a communicative environment. Examples of diagnosis using applicable rules illustrate the functioning of this architecture.
