Intelligent tutoring systems can support students in solving multi-step tasks by providing a hint regarding what to do next. However, engineering such next-step hints manually or using an expert model becomes infeasible if the space of possible states is too large. Therefore, several approaches have emerged to infer next-step hints automatically, relying on past student’s data. Such hints typically have the form of an edit which could have been performed by capable students in the given situation, based on what past capable students have done. In this contribution we provide a mathematical framework to analyze edit-based hint policies and, based on this theory, propose a novel hint