In this article, digital transformation is examined as a key driver of structural and pedagogical change in higher education. This process is shown to expand access to learning, increase flexibility, support personalized educational trajectories, and enhance data-driven decision-making. At the same time, the effectiveness of digital transformation depends on institutional readiness, the quality of technological infrastructure, and the professional competencies of teaching staff. This research of this study is to assess the influence of digital transformation on the quality of higher education. This research employs a mixed-methods approach. Quantitative data from surveys of 4971 students and 483 instructors were analyzed using descriptive statistics, analysis of variance ANOVA, and multivariable regression, while qualitative focus group findings were examined through thematic analysis. The results indicate generally positive attitudes toward digitalization. The respondents emphasized flexibility and improved conditions for independent learning as key advantages of digital environments. However, this study also identifies several challenges, including infrastructural inequality, limited digital skills, and insufficient pedagogical adaptation. The article concludes that successful digital transformation requires a comprehensive strategic vision and sustained institutional support. For universities, strengthening digital competencies, modernizing infrastructure, and implementing management models focused on continuous improvement are essential conditions for ensuring sustainable development and enhancing the quality of education.

This paper proposes the creation of an intelligent system for assessing the levels of professional competencies of students in e-learning. Mathematical models and methods for assessing the formation of professional competencies of students at the level of disciplines, modules and the entire educational program are built. Algorithms for calculating the assessment of levels of competency in the discipline, module, and educational program are developed. These estimates can be used in the formation of inference rules for making decisions on changing the structure and content of disciplines, modules and educational programs.

This study investigates how targeted attacks can compromise the reliability and applications of large language models (LLMs) in educational assessment, highlighting security vulnerabilities that are frequently underestimated in current AI-supported learning environments. As LLMs and other AI tools are increasingly being integrated into grading, providing feedback, and supporting the evaluation workflow, educators are adopting them for their potential to increase efficiency and scalability. However, this rapid adoption also introduces new risks. An unexplored threat is prompt injection, whereby a student acting as an attacker embeds malicious instructions within seemingly regular assignment submissions to influence the model’s behaviour and obtain a more favourable evaluation. To the best of our knowledge, this is the first systematic comparative study to investigate the vulnerability of popular LLMs within a real-world educational context. We analyse a significant representative scenario involving prompt injection in exam assessment to highlight how easily such manipulations can bypass the teacher’s oversight and distort results, thereby disrupting the entire evaluation process. By modelling the structure and behavioural patterns of LLMs under attack, we aim to clarify the underlying mechanisms and expose their limitations when used in educational settings.

This article describes the development of the system for the automatic generation of test questions based on the knowledge base. This work has an applicable nature and provides detailed examples of the development of ontology and implementation the SPARQL queries in RDF-documents. Also it describes implementation of the program generating questions in the Python programming language including the necessary libraries while working with RDF-files.

The article presents the results of the application of an ontological approach to the description of the structure and content of the educational programme, and its subsequent analysis for the consistency of prerequisites and learning outcomes of courses. The practical result of the work is an ontology approach implemented in the Protégé 5.5.0 editor, which reflects the studied disciplines in terms of the skills they form and the entrance requirements (prerequisites) for the qualification of the student. The curriculum model includes sequences of semesters and courses of study (academic year) related by time relationships. The developed ontology approach is filled with data from the educational programme “Software Engineering”. The authors have earned queries in DL Query and SPARQL languages, which, using logical inference procedures, make it possible to analyse an educational programme for consistency of disciplines in terms of input requirements and the skills of the learner formed during the training period. The developed ontology and rules of logical inference can be used as a part of the educational process management information systems and educational programme designers, for the intellectual analysis of programme integrity and the consistency of learning prerequisites and outcomes in disciplines.