One of the main principles underlying the design of human–system interactions within ITSs or ILEs is that the closer the “artificial” principles are to those involved by human teachers, the more efficient the learning will be. However, the very notion of “human-likeliness” is neither very new nor very precise. We suggest here that these human-like interactions need to be grounded in the very core human social capabilities, notably those allowing mind reading. We address this problem here, first in reviewing the literature about tutoring principles, then in proposing a new classification scheme of these principles. Third, we sketch the first lines of a model and a LSA-based software architecture that attempts to comply with this “human-likeliness” by providing teacher-like advice for learning courses.