A major weakness of serious games at the moment is that they often incorporate multiple choice questionnaires (MCQs). However, no study has demonstrated that MCQs can accurately assess the level of understanding of a learner. On the contrary, some studies have experimentally shown that allowing the learner to input a free-text answer in the program instead of just selecting one answer in an MCQ allows a much finer evaluation of the learner's skills. We therefore propose to design a conversational agent that can understand statements in natural language within a narrow semantic context corresponding to the area of competence on which we assess the learner. This feature is intended to allow a natural dialogue with the learner, especially in the context of serious games. Such interaction in natural language aims to hide the underlying MCQs. This paper presents our approach.