For an intelligent tutoring system (ITS) to earn its "I", it must be able to (1) accurately diagnose students' knowledge structures, skills, and/or learning styles using principles, rather than pre-programmed responses, to decide what to do next; and (2) adapt instruction accordingly. While some maintain that remediation actually comprises the "T" in ITS, this paper takes the position that the two components (diagnosis and remediation), working in concert, make up the intelligence in an ITS. A framework for developing and assessing student models is presented, followed by a description of an attempt to apply the framework in the development of a student model incorporated within a non-intelligent computer tutor. The two systems (with and without a student model) are compared in terms of outcome and efficiency measures. The framework is an adaptation of Dillenbourg and Self's (1992) two-dimensional framework and notation for student modeling, which was modified to represent specific knowledge and skill types required during the learning process, procedural skills, conceptual knowledge rather than overt behaviors, and cognitive process measures. The horizontal axis remains basically the same as the original: learner's representation of the knowledge or the skill, system's representation of the learner's knowledge, and the system's/expert's representation of the knowledge or skill. This modified framework represents the standard microadaptive approach to student modeling. The intelligent and non-intelligent version of "Stat Lady," an experiential learning environment and curriculum that teaches statistical concepts and skills, are described. The "Stat Lady" versions provide the basis for a planned experiment testing the degree to which inclusion of a student model may enhance learning outcome measures and/or improve learning efficiency. (Contains 16 references.) (Author/MAS)