 This paper discusses the stages involved in developing an intelligent system for evaluating multiple-level test tasks using fuzzy logic in the MATLAB application package. It first presents the advantages and disadvantages of existing approaches to fuzzy assessment of test methods. Then, it describes two methods for assessing students, using fuzzy sets and corresponding membership functions, and fuzzy estimation and generalized fuzzy estimation methods. Next, the Segino production model is used as the closest to natural language, allowing for closer interaction with a subject matter expert in building well-understood, easily interpreted inference systems. The structure of a fuzzy system, its functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four inputs, corresponding to different levels of knowledge assimilation and one initial variable. The surface of the response of the fuzzy system reflects the dependence of the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure and functions of the fuzzy system are indicated. The modeled intelligent system for assessing multiple-level test tasks based on fuzzy logic makes it possible. This article was authored by Ivan M. Sidolo, Sirhi Osamerikov, Tashiana Igargula and others. We are article.tv, links in the description below.