The purpose of the study. The paper discusses the current issues of checking the volume of borrowed text in the graduation qualification paper in a technical university taking into account the probability of using the capabilities of artificial intelligence by students. The problem of plagiarism, in particular plagiarism of graduation qualification papers (diploma theses), has always been actual. Some students, when writing their graduation qualification papers, tend to copy texts of papers defended in previous years, which led to the need to organize plagiarism check of texts of all papers of the current year of graduation. There are various methods that make it possible to easily bypass such a check. This problem has become especially actual in recent years due to the development of information technology. The widespread introduction of generative artificial intelligence has led to the emergence of a new problem the need for the supervisor and/or designated responsible person to check the graduation qualification paper for the presence of text generated by artificial intelligence. This paper discusses the features of plagiarism check of texts of graduation qualification papers of students studying in the areas related to information technologies, considering the potential possibility of using generative artificial intelligence by students, in particular ChatGPT and GitHub Copilot software. Materials and methods. The method of comparative analysis of scientific publications for plagiarism checking and the use of artificial intelligence in the educational process was used. Existing plagiarism checking methods are irrelevant when checking texts generated by artificial intelligence. The attributes and examples of such texts are considered. Trends in the environment of students at a technical   university in relation to the use of generative artificial intelligence, in particular ChatGPT and GitHub Copilot software when writing graduation qualification papers were experimentally identified. The possibilities of applying a number of programs for detecting texts generated by artificial intelligence have been verified. Research results. The analysis of plagiarism check results for texts generated by an artificial intelligence system and prepared by a methodologist was carried out. The problem of unambiguous automatic detection of the use of generative neural networks by students in the process of preparing a graduation qualification paper due to the presence of false positives was discussed. It seems advisable to widely implement systems for checking the text of graduation qualification paper for the presence of text generated by artificial intelligence systems. However, the test use of existing verification systems showed that the reliability of checking for the presence of text generated by artificial intelligence systems is highly debatable. The percentage of identified borrowings can vary both downwards and upwards with incorrect conclusions. The problems caused by the peculiarities of teaching students at a technical university are discussed. A path for checking the materials of graduation thesis for AI plagiarism is proposed. Conclusion. The importance and necessity of checking the originality of the graduation qualification papers for borrowings both the texts of the graduation qualification paper of previous years and the use of texts and programs generated by artificial intelligence systems are outlined. The authors propose possible approaches to organizing the educational process at a technical university taking into account the accumulated experience, as well as ways to solve the problems discussed in the paper, in particular, the introduction of mandatory marking of both the text and the program code created by the artificial intelligence system is proposed. In addition, the need to develop relevant teaching methods, including the formation of reflexivity, is emphasized.

The purpose of the study is to theoretically substantiate the importance of introducing electronic library systems into the educational process with an emphasis on the formation of information competence of students in higher education institutions. With the rapid digitalization of the educational environment, it becomes necessary to identify key skills that will help students effectively interact with information resources. The research aims to identify the advantages of using electronic library systems as a tool for developing critical thinking and overcoming barriers to mastering modern information technologies. The materials and research methods include the analysis of literary sources and normative documentation, which allowed us to formulate the theoretical foundations of the formation of information competence among students. The application of the competence approach contributed to the systematization of knowledge about the necessary skills of working with information. To illustrate the successful application of electronic library systems in the educational process, a case method was used, which demonstrates the practical significance of the research. A comparative analysis was also used to identify the correspondence of skills of working with electronic library systems to indicators of key competencies of students. The results show that in the era of total digitalization of the educational process, the competence approach remains an effective strategy that provides systematic monitoring and support for the development of students’ competencies at all stages of learning, starting with the formation of basic skills and ending with the achievement of professional skills. There is a growing interest in information competence among researchers and lecturers, which makes it a priority strategy for the development of educational systems. Electronic library systems at the legislative level have become integral components of the electronic information and educational environment of the university. However, their implementation in the educational process is associated with a number of difficulties that require study and overcoming for widespread use. The positive experience of introducing modular training of students to work with electronic library systems is considered. The correspondence of the skills acquired by students when working with library systems to indicators of achieving key competencies is clearly demonstrated, which confirms the positive impact of such training on the formation of information competence as a key personality trait in the era of digitalization. The conclusion emphasizes that the formation of information competence of future specialists at the training stage is a prerequisite for successful professional activity. This is consistent with the ideas of advanced education and requires a transformation of the education system. Teaching the principles of information systems through information and communication technologies in the electronic information and educational environment of the university plays an important role in this process, developing information competence and preparing students for professional activity in a digital society. Ultimately, such changes will contribute to the creation of a more flexible and competitive education system that meets the challenges of the modern era.

Purpose of research. The purpose of the article is to substantiate the application of the developed model for the diagnosis of cognitive activity in the secondary vocational education system when students master the academic discipline “Computer science”. Research materials and methods. The idea of the research is related to the application of the developed model for the diagnosis of cognitive activity in the secondary vocational education system when students master the program of the general education discipline “Computer science”. The implementation of the developed diagnostic model is based on the synthesis of highly formalized and low-formalized methods in order to obtain the most reliable picture of the level of cognitive activity of the contingent in computer science classes. The developed model of cognitive activity diagnostics allows us to obtain pedagogically significant information that characterizes the dynamics of changes in the level of cognitive activity when performing special psychological and pedagogical tests, as well as educational tasks, and to compile a reliable picture of the effectiveness of the educational   process in the studied groups of students of secondary vocational education, taking into account cognitive, pedagogical, social criteria, and also the level of basic computer science training. The model for diagnosing the level of learning in computer science and cognitive activity includes the assessment of the acquired knowledge, the development of professional skills and encouraging students to continuously improve and apply professional skills on a regular basis. At the same time, boundary control is an indicator of the level of knowledge of the educational material content, intermediate control is a demonstration of mastering practical skills, and stimulating   points introduce a component of motivation, which affects the level   of cognitive activity of the student. Cognitive activity in the context of the study is defined as a cognitive, psychological and social response to the cognitive process, which determines the personal and motivational interest in the conscious   acquisition of knowledge and skills of the subject area and is a structural component of the effectiveness of the educational process in computer science. Results. Based on the revealed essence of cognitive activity in subject training, a model for diagnosing the cognitive activity of students of secondary vocational education in computer science classes has been compiled, substantiated and tested in the real educational process, allowing for a reliable picture of the effectiveness of the educational   process in the studied groups, taking into account cognitive,   psychological and social criteria, as well as the level of initial training   in computer science. Conclusion. The application of the developed model for diagnosing cognitive activity using a component-by-component expert assessment of intellectual initiative in the context of the effectiveness of computer science teaching contributes to the personalized and group identification of students in the secondary vocational education system, and also allows for comparative assessments of the final rating indexes in each of the individual groups of students in order to adjust and supplement selected teaching methods and tools. The materials of the paper can be useful for lecturers of secondary and higher professional education.

The purpose of the study is to develop methods for implementing a multi-agent system of a network enterprise within the framework of a microservice architecture of the Industrie 4.0 digital platform. In this regard, methods for implementing agents using services that automate the functions of manufacturing and business processes, and services that perform the functions of a digital platform are proposed. The development of software agents is based on the structure of Asset administrative shells used in active and proactive modes. Materials and methods. As a research method, it is proposed to use an improved method of functional design based on the architectural frameworks of the Industrial Internet Consortium (IIRA) and the Platform Industrie 4.0 (RAMI), Russian state standards (“Digital twins”, “Digital factory”, “Smart production”), as well as a method for analyzing enterprise capabilities. The results of the study. The main results of the article are the construction of the component composition diagram of the digital platform conceptual model, algorithms for generating a request to produce product components and its assessment by software agents of the digital platform in the form of UML sequence diagrams. Conclusion. The results of the article allow to increase interoperability and flexibility in the configuration of value chains based on the service implementation of a multi-agent system.

This paper examines the role of artificial intelligence (AI) in the development of the digital economy. It analyzes key areas of AI use in various industries: from forecasting market trends and optimizing production processes to improving the efficiency of logistics and financial transactions. Particular attention is paid to machine learning models that allow analyzing large volumes of data for strategic decision-making. It also touches on the challenges associated with the implementation of AI, including cybersecurity, job losses, and ethical aspects. The paper presents practical examples of using AI to analyze the market and assess the impact of automation on employment. The result of the study was the generalization that the competent implementation of AI contributes to increasing the competitiveness of countries, accelerating innovation, and sustainable economic growth in the context of global digitalization. Materials and methods. There are several simple methods that can be used in Python to perform market analysis using artificial intelligence, including data analysis using libraries such as pandas for data processing and scikit-learn for machine learning. One of the simplest options is trend analysis using the regression method. Here is an example of simple code for market analysis using linear regression. Results. Finally, we create a model that predicts sales volume based on the price of a product. We use simple linear regression to analyze the relationship between price and the number of units sold. The code also visualizes the relationship between price and sales volume, and outputs the model coefficients. Conclusion. Using artificial intelligence methods such as linear regression allows us to effectively analyze market trends and identify relationships between key indexes such as price and sales volume. Using Python libraries such as pandas and scikit-learn simplifies data processing and construction of predictive models. Visualization of results helps to better interpret the obtained dependencies, which can be a useful tool for making informed management decisions and optimizing marketing strategies.

Scientific relevance of the study. In the era of rapidly increasing volumes of data generated by social media users, analyzing textual data such as comments is becoming one of the key challenges of modern science. Comments are a valuable source of information, allowing us to identify public sentiment, analyze users’ opinions, and track social trends. However, due to the semistructured or completely unstructured nature of these data, their processing requires innovative approaches. Purpose of research. The aim of this research is to develop an intelligent system for processing semistructured data from comments on social media videos using structuring algorithms targeting different industries. The research aims to create an efficient method to analyze tone, clustering and extract key themes from comments in order to evaluate the impact of video content on the audience. The research will propose an approach to automatically extract and structure data by industry, which will allow for a more accurate and in-depth analysis of content perception and its impact on different social and professional domains. Methods. Developing an intelligent system for analyzing semistructured data requires innovative methods and approaches that combine natural language processing (NLP), machine learning algorithms and big data analytics techniques. These methods include: automatic data extraction via API, preprocessing adapted for three languages (French, English and Russian), deep sentiment analysis using the Bert product and a probabilistic algorithm for statistical calculations, and clustering using K-Means, DBSCAN and Agglomerative algorithms. The materials are based on comments from social networks (TikTok, Instagram, Twitter, Facebook, YouTube, Reddit, VKontakte) in   Russian, English and French. SpaCy and NLTK libraries were used for preprocessing, and the Hugging Face Transformers model worked with pre-trained models for sentiment analysis. Machine learning techniques including clustering and natural language processing were used. Data was structured using topic modeling and language models implemented using Python libraries. The results of the study. The development of an intelligent system for processing semistructured data has improved the analysis of comments on videos in social networks through a combination of various machine learning models and algorithms. The results of the study allowed us to develop a prototype of a comment analysis tool that effectively collects   and structures data from various social networks. This data structuring led to better organization and increased accessibility of information, facilitating its utilization. By using natural language processing (NLP) methods, we identified key themes and emotions in the comments while conducting sentiment analysis that highlights major emotional trends. Clustering methods, such as K-means, grouped the comments by similar themes. Additionally, we created visualizations that show sentiment distribution, allowing users to quickly interpret the data. The integration of visualization techniques transforms complex analytical results into intuitive graphs, making it easier to understand user interactions with the content. Thus, our system proves effective in providing valuable insights and optimizing audience interaction strategies. Conclusion. The results of the study showed that the proposed approach significantly improves the accuracy of classification and structuring of semistructured data, especially when it comes to comments extracted from social media videos. The developed system uses natural language processing algorithms to analyze the data with respect to its industry, which allows for automatic structuring of comments depending on their content and detailed tone analysis. The effectiveness of this approach was validated by analyzing comments from various social platforms, which demonstrated its ability to extract and structure relevant information, as well as assess the impact of videos through user reactions.