Modeling the user's state of mind in tutoring systems is primarily a matter of determining which of the targeted concepts and skills have, or have not yet, been acquired by the user, and diagnosing misunderstandings that are likely to have occurred, given the session transcript so far. Some recent experimental systems can also adapt their strategies to the user's apparent mood, such as frustration or boredom, as might be revealed by the user's inputs, tone of voice, or even facial expressions or gestures analyzed via computer vision.  Other prototype systems can be viewed as striving towards more general mental modeling, by incorporating ideas and techniques from task-oriented dialogue systems concerning dialogue states, dialogue acts, and deeper language understanding (e.g., Callaway et al. 2007).