In Educational literature, Discovery Learning appears as an approach in which the learner builds up his/her own knowledge by performing experiments within a domain and inferring/increasing rules as a result. Such a constructivist approach has been largely exploited in the design of computational artifacts with learning purposes in what is today known as Discovery Learning Environments (DLE's). In this paper we are concerned with the design and usage of particular DLE's, within which learning events occur as a consequence of contradiction detection and overcoming, during human/machine cooperative work. We firstly introduce a model of an agent capable of handling such an approach of learning, by highlighting the exchanges that the agent should promote with a human learner. The model lies on the basis of the scientific rationale, particularly the empirical approach guided by the theory-experiment confrontation. We shall then reinforce the interest of the model for the design of DLE's, by presenting its exploitation in a real learning situation in Law.