Abstract <p>This article is devoted to the application of machine learning methods to improve the quality of test items. A review of the subject area has been conducted, and two methods for improving quality have been implemented: similar question retrieval and distractor quality assessment. The former involves testing five transformer-based models to generate text embeddings and six clustering algorithms. The latter uses the same transformer models in combination with three classification algorithms. Experimental results demonstrated high effectiveness of the proposed approaches in solving both tasks.</p>