Abstract <p>The development of automatic assessment systems is a relevant task designed to simplify the routine work of a teacher and speed up feedback for a student. This paper reviews research in the field of automatic assessment of students’ answers based on the teacher’s reference answer. The authors of the work analyze text models used for the tasks of automatic short answer grading (ASAG) and automated essay scoring (AES). Several approaches are also taken into account for the task of determining the text’s similarity, since it is a similar task, and the methods for solving it can also be useful for analyzing students’ answers. Text models can be divided into several large categories. The first category consists of linguistic models based on various stylometric features, both simple ones, such as a bag-of-words and n-grams, and complex ones, such as syntactic and semantic features. The authors attribute neural network models based on various embeddings to the second category. It highlights large language models as universal, popular, and high-quality modeling methods. The third category includes combined models that unite both linguistic features and neural network embeddings. A comparison of modern studies on models, methods, and quality metrics shows that the trends in the subject area coincide with the trends in computational linguistics in general. A large number of authors choose large language models to solve their problems, but the standard features remain in demand. It is impossible to single out a universal approach; each subtask requires a separate choice of method and adjustment of its parameters. Combined and ensemble approaches allow achieving higher quality than other methods. The vast majority of studies examine texts in English. However, successful results for national languages are also found. It can be concluded that the development and adaptation of methods for assessing students’ answers in national languages is a relevant and promising task.</p>