In Case Based Reasoning (CBR), knowledge acquisition plays an important role as it allows to progressively improve the system's competencies. One of the approaches of knowledge acquisition consists in performing it while the system is used to solve a problem. An advantage of this strategy is that it is not to constraining for the expert: the system exploits its interactions to acquire pieces of knowledge it needs to solve the current problem and takes the opportunity to learn this new knowledge for future use. In this paper, we present two approaches of interactive knowledge acquisition in CBR. Both approaches rely on the exploitation of reasoning failures. Indeed, an interactive learning process aiming at correcting the solution and at learning new knowledge is triggered when a reasoning failure occurs.