Intelligent tutoring systems (ITS) provide individualized instruction. They offer many advantages over the traditional classroom scenario: they are always available, non-judgmental and provide tailored feedback resulting in increased and effective learning. However, they are still not as effective as one-on-one human tutoring. The next generation of intelligent tutors is expected to be able to take into account the cognitive and emotional state of students. This paper presents a proposed contribution of affect to student modeling, and reports on the progress made in the development of a facial expression analysis component for intelligent tutoring systems.