Bulletin of NTU "KhPI". Series: Strategic management, portfolio, program and project management

THE EVOLVING LANDSCAPE OF EDUCATION UNDER THE INFLUENCE OF AI

Sergey Bushuyev — 2025-03-17

The subject of research explores the transformative impact of Artificial Intelligence (AI) on education, tracing its evolution and analysing its current and potential future implications. With the rapid advancements in AI technologies, education systems worldwide are undergoing significant changes, affecting teaching methodologies, learning experiences, and educational outcomes. This paper examines how AI is reshaping various aspects of education, including personalized learning, adaptive assessment, intelligent tutoring systems, and administrative tasks. Additionally, it discusses the ethical considerations, challenges, and opportunities associated with integrating AI into education. Through an interdisciplinary lens, this paper synthesizes insights from educational psychology, computer science, and pedagogy to provide a comprehensive understanding of the evolving landscape of education in the AI era. The result of the study offers recommendations for policymakers, educators, and researchers to harness the potential of AI while addressing its potential pitfalls, ensuring that education remains inclusive, equitable, and learner-centred in the digital age. Artificial Intelligence (AI) is rapidly transforming the educational landscape, prompting excitement and apprehension. This paper explores the potential of AI to revolutionize education by offering personalized learning, adaptive instruction, enhanced engagement, and automated feedback. The integration of AI also presents significant challenges regarding ethical considerations, teacher training, accessibility, and cost.

DEVELOPMENT OF INFORMATION TECHNOLOGY FOR INTELLIGENT PLANNING OF THE IT PROJECT TEAM'S WORK BASED ON A FLEXIBLE METHODOLOGY

Marina Grinchenko — 2025-03-17

The subject of the study is the development of information technology for intelligent planning of the IT project team. The purpose of the study is to reduce project risks associated with the perception of tasks and their distribution among team members within the project sprint by introducing methods and models for intelligent planning of the IT project team. The information technology is developed on the basis of a set of methods and models, namely: a method for intelligent planning of the IT project team; a model for evaluating a textual description of a task; a method for improving textual descriptions of project tasks; a model for distributing sprint tasks among performers; a method for generating recommendations when planning the work of an IT project team. The use of models related to the processing of textual descriptions of project tasks provides a better understanding of the tasks by the team and increases the efficiency of project implementation. The use of artificial intelligence, including large language models, and stable distribution algorithms in management processes contributes to the automation and efficiency of working with project sprint tasks, increases productivity and team cohesion. The result of the study is an information technology based on the integration of artificial intelligence into sprint planning, task allocation, and risk management. The application of the proposed information technology has significant potential to increase project flexibility, efficiency, and overall success. The developed information technology takes into account the project context and expert opinions, which makes it flexible and adaptive to the specifics of a particular project and team characteristics, which is important for further improving project management processes. The information technology for intelligent planning of the IT project team's work will you to assess and reduce the negative impact of factors that threaten the timing and quality of the project by analyzing historical data and modeling team behavior.

ANALYSIS OF HIGHER EDUCATION INSTITUTIONS' PERFORMANCE INDICATORS BASED ON QS WORLD UNIVERSITY RANKINGS ASSESSMENT

Marina Grinchenko — 2025-03-17

In the modern context of globalization and increasing competition among universities, a key factor for the successful development of higher education institutions (HEIs) is the ability to accurately assess their performance. This study provides a review of research related to global ranking assessments and performance management of HEIs using key performance indicators (KPIs), substantiating the relevance of this research. The aim of the study is to improve the system of key performance indicators (KPIs) at the National Technical University "Kharkiv Polytechnic Institute" (NTU "KhPI"), which will contribute to enhancing the quality of educational services and improving the university’s position in international rankings. The task of the study is to establish the relationship between the planned target indicators defined in the university rector’s contract and the QS World University Rankings (QS-WUR) indicator system, which influences the institution’s position in this global university ranking. Based on an analysis of NTU "KhPI" performance according to the QS-WUR methodology, an approach for formalizing the QS-WUR indicators that determine its ranking position is proposed. The developed model for forming QS-WUR ranking indicators for NTU "KhPI" explains who provides the information for calculating each indicator and illustrates the interconnections between these indicators in the university evaluation process. The study also formalizes the performance results of NTU "KhPI," which are annually published on the university's official website and calculated based on the performance indicators of its institutes, departments, and other units. This comprehensive approach to evaluating university performance allows for the identification of strengths and weaknesses in managing scientific and international activities and organizing the educational process. The implementation of the improved KPI system at NTU "KhPI" will facilitate the optimal allocation of resources, the introduction of innovative approaches in academic, research, and international activities, and, in turn, ensure high standards of education quality and international recognition of the university.

EXPLORING THE CONCEPT OF DERIVATIVE RISKS ARISING FROM EXTERNAL INFLUENCES IN THE CONTEXT OF BUSINESS OPERATIONS AND THEIR STRATEGIC STABILITY

Vadym Ziuziun — 2025-03-17

In the modern operational environment, external factors play a decisive role in shaping the strategy and tactics of enterprise development. The dynamic changes in market, economic, political, social, and technological parameters necessitate the development of a systematic approach to analyzing the impact of external factors on internal business processes. One of the key aspects of such analysis is the assessment of derivative risk from influence development, which can be formalized as a functional dependence between external influences, business processes, and their outcomes. The objective of this study is to develop and substantiate a mathematical model for analyzing the development of external influences on the activities of business entities. This model aims to systematize the process of risk assessment and management through the use of quantitative analytical methods. The study formalizes the key components of the model, including the set of external influences, business processes, derivative risks, and the final state of the affected entity. The proposed model takes into account key external environmental factors – economic, political, social, environmental, and technological – and their impact on core business functions, such as operations, financial management, marketing, supply chain management, and human resources. Particular attention is paid to the mechanism of derivative risk formation, which is expressed through a system of equations that describe the dependencies between external influences and the dynamics of changes in business processes. Based on the mathematical framework, which includes linear and nonlinear dependencies, probabilistic analysis, and optimization methods, a system for quantitative and qualitative risk assessment has been developed. This approach enables effective risk forecasting and the formulation of optimal response strategies. The probabilistic approach facilitates the modeling of potential scenarios and the selection of the best decision-making options. The practical significance of the model lies in its application for strategic planning by identifying potential threats and opportunities, risk management based on quantitative analysis of their impact on business processes, resource optimization to minimize potential losses, enterprise adaptation to dynamic external changes, and enhancing operational efficiency through flexible business processes and proactive response strategies. Thus, the results of this study can be utilized as an effective tool for enhancing enterprise resilience, optimizing operations, and developing crisis management measures in response to external challenges.

ANALYSIS OF COMPUTATIONAL INTELLIGENCE METHODS FOR MODELING, IDENTIFICATION, OPTIMIZATION OF SYSTEMS AND DECISION SUPPORT

Oleksii Kondratov — 2025-03-17

The latest methods and tools of computational intelligence that have found widespread application in various fields, including information control systems, decision support systems, as well as modeling and remote identification of dynamic system, have been analyzed. Special attention is given to methods such as HunyuanVideo, emg2pose, StableAnimator, DEYO, YOLOv11, YOLO-NAS, SynCamMaster, FlowNet, Momentum-GS, Liger-Kernel, Stereo Anywhere, and Neural Attention Memory Models. The analysis shows the great potential of these technologies for improving existing solutions in the field of computational intelligence. HunyuanVideo uses diffusion models for video generation, significantly improving visualization and dynamics while reducing computational power requirements. The emg2pose and StableAnimator methods provide high precision and flexibility, which are especially important for real-time decision support systems. The application of technologies such as DEYO and YOLOv11 has improved the speed and accuracy of object detection, which is crucial for security and real-time video stream monitoring. The FlowNet and FlowNet 2.0 methods for optical flow estimation allow precise tracking of object motion, significantly improving the accuracy in dynamic scene processing. SynCamMaster synchronizes video from different viewpoints, opening up new opportunities for 3D scene reconstruction, demonstrated through the use of technologies like Momentum-GS. At the same time, specialized strategies such as Liger-Kernel are actively applied to enhance efficiency in complex environments like autonomous vehicles and robotics. The necessity of optimizing computational processes for integrating these methods into real-world systems is discussed, with a focus on ensuring high precision and speed of technology operation under resource constraints. The use of these technologies will enable the creation of innovative approaches to solving complex real-time problems, significantly improving the effectiveness and accuracy of existing systems. The included tables demonstrate the importance of integrating new technologies into various research fields.

A MODEL FOR SELECTING ARTIFICIAL INTELLIGENCE TOOLS TO SUPPORT SOFTWARE DEVELOPMENT PROCESSES

Andrii Kopp — 2025-03-17

Integrating artificial intelligence (AI) tools into software development projects significantly improves the efficiency of various tasks within the software development lifecycle (SDLC). AI-driven tools embedded in integrated development environments (IDEs) improve developer productivity and code quality, and facilitate better interaction between project participants and AI-based systems. The main research directions for integrating AI into software development processes include adapting user interfaces for specific tasks, increasing trust in AI-based systems, and improving code readability. AI enhances several SDLC stages, including automated code generation, code review and defect prediction. Implementing AI tools in IDEs accelerates development, improves code quality and reduces defects. Machine learning and natural language processing play a critical role in improving software quality through requirements classification and defect prediction. AI-based solutions, such as recommendation systems and chatbots, support various software development processes, including requirements gathering. Therefore, a relevant scientific and practical challenge is to create a model for the justified selection of AI tools to support software development processes in order to improve project efficiency. This study proposes a mathematical model that minimizes the cost of using AI tools, while ensuring compliance with minimum requirements that affect project efficiency. The optimization model takes into account criteria such as pricing, integration, support and functionality capabilities, using normalized evaluations based on Gartner Peer Insights and other open sources. The objective function minimizes the total cost of AI tools, subject to constraints that ensure minimum acceptable evaluation scores. The developed approach enables a systematic selection of AI tools, thus improving the efficiency of software development projects.

DEVELOPMENT OF A WEB APPLICATION FOR CREATING DOCUMENTATION FOR TESTERS AND EVALUATION OF ITS IMPACT ON THE EFFECTIVENESS OF TESTING IN IT PROJECTS

Anton Lysenko — 2025-03-17

The development of a web application for creating documentation for testers is a crucial aspect of improving the efficiency of the testing process in IT projects. Modern IT projects are becoming increasingly complex, necessitating tools that can ensure a clear and structured approach to creating, maintaining, and updating test documentation. This documentation is an integral part of the quality assurance process and plays a key role in the interaction between team members. The aim of this paper is not only to describe the functional capabilities of the web application but also to assess its real impact on testing efficiency in IT projects. The creation of a specialized web application enables the automation of routine processes, such as documenting test scenarios, bug reports, and testing outcomes. This reduces the time spent on manual execution of these tasks and significantly minimizes the risk of human error. Furthermore, automation enhances the accuracy and efficiency of testing by allowing testers to focus on critical aspects of their work instead of mechanical tasks. The developed web application integrates project management functionalities, allowing for more efficient planning, distribution, and control of testing tasks. For example, features such as creating interactive test scenarios, managing test priorities, and monitoring progress enable teams to adapt to project changes in real-time. This, in turn, improves communication between testers, developers, and other stakeholders, ensuring transparency in the testing process. The paper also examines the impact of implementing the web application on team productivity. A comparative analysis was conducted between the time required for manual documentation and the time needed to create the same documents using the automated tool. The results showed that automation significantly reduces the time spent on documentation while simultaneously improving the quality and completeness of the generated reports. Additionally, the prospects for further improvement of the web application and its integration with other IT project management tools are discussed.

DEVELOPMENT AND IMPLEMENTATION OF AN ELECTRONIC DOCUMENT MANAGEMENT INFORMATION SYSTEM IN A LOCOMOTIVE DEPOT AND APPLICATION OF PROJECT MANAGEMENT PROCESSES

Olena Lobach — 2025-03-17

Automation of enterprise activities is one of the key directions in the development of information technologies under modern conditions. However, several industries, including the maintenance of electric trains, still rely on outdated methods for managing production processes. This creates significant challenges, as this sector is associated with a high level of responsibility and potential risks. Even minor errors can lead to severe financial losses or employee injuries. One of the most pressing issues remains document management. In many cases, the transmission of critical information is carried out via phone calls, and responsibility zones are monitored using paper logs, which are often neglected. Such an approach significantly reduces operational efficiency and increases the risk of errors. The implementation of an electronic document management system has significant potential to address these issues. Such a system allows for the automation of data transfer processes, reduces the costs associated with paper documentation, optimizes workshop operations, and improves the allocation of employees’ working time. Moreover, it ensures accurate control over task execution and reduces the risk of human error, thereby enhancing the overall efficiency of the enterprise. The goal of this work is to develop and implement an electronic document management system for a locomotive depot. The program implementation is supported by the following tools: the JavaScript programming language, the React.js library, the Node.js platform, Prisma ORM, and the PostgreSQL database management system. The proposed system is expected not only to lower material costs but also to establish a modern technological foundation for improving the safety and reliability of the enterprise's operations. Additionally, it will facilitate more accurate planning, monitoring, and analysis of workflows, ensuring adaptation to the growing demands of the market.

DEVELOPMENT OF THE INFORMATION SYSTEM FOR SOLVING THE PROBLEM OF RENTING AND BUYING HOUSING IN THE NETHERLANDS WITH THE INTRODUCTION OF A CONTEXTUAL SEARCH ALGORITHM: PERSONALIZED AND ACCELERATED SELECTION OF REAL ESTATE

Anastasiia Lozinska — 2025-03-17

The current state of the real estate market in most European countries, including the Netherlands, requires the introduction of innovative solutions that can facilitate and speed up the process of finding housing and meet the needs of its applicants. This is largely due to the lack of reliable, up-to-date data and the neglect of the need to adapt existing web applications and information systems for foreigners who do not speak Dutch, which is very important in the context of growing migration. In addition, existing solutions often offer information about real estate that varies depending on the user's preferences, including information about their age and nationality, which leads to discrimination. In view of this, it becomes important to introduce applications that contain detailed, up-to-date, and reliable data on residential properties, as well as allow customizing the process of selecting a home to meet the individual needs of users. The purpose of this paper is to develop an information system for solving the problem of renting and buying housing in the Netherlands, to create a contextual search algorithm to speed up the search for real estate, and to implement it in this web system. The algorithm consists of three main stages. The first step involves geocoding the address entered by the user. The second stage involves sending a request to Google Places, calculating the search radius based on the boundaries of the square, and then using the Gaversus formula, and checking the objects contained in the service's database for their inclusion in the calculated area. The third step involves sending the coordinates of the first received object and the type of real estate to the server. If ads are found in the information system database, they are displayed to the user. The developed information system and the contextual search algorithm implemented in it allow providing home seekers with detailed, up-to-date and reliable information about real estate available for rent and purchase, speeding up the process of finding a home and meeting the needs of home seekers. The main functionality of the web system also includes the ability to publish, modify, delete ads and establish communication with their authors.

APPLICATION OF THE GENERAL ALGORITHM OF LINEARIZATION IN LINEAR FRACTIONAL OPTIMIZATION PROBLEMS IN PROJECT MANAGEMENT

Liubava Chernova — 2025-03-17

Effective planning of resources and optimization of the work schedule allows you to minimize costs and adhere to project deadlines, which ensures the quality of results. Many real projects include complex interdependencies and constraints that can be described by nonlinear models, complicating the process of their optimization. The use of a general linearization algorithm for nonlinear optimization problems offers an innovative approach to simplifying and solving complex planning problems. Linearization makes it possible to transform non-linear models into linear forms that are more convenient for calculation, which facilitates the application of linear programming methods. This is especially important in conditions of limited resources and strict constraints on the time and budget of the project. If the project has a complex schedule with many interdependent tasks, as well as limited resources, we believe that the original model contains nonlinear constraints, for example, dependencies between different tasks that affect the duration and use of resources. Using linearization techniques, such as replacing nonlinear constraints with linear approximations or using partial derivatives for local linearization, the problem can be simplified to a linear form. After linearization, linear optimization methods, such as linear programming, are used to determine the optimal resource allocation and task execution schedule.One of the most common examples of using the linear fractional optimization in project management is given by a problem of minimizing the expense per a unit of time or resource while maximizing the tasks completion quality. For example, in planning of a construction project, managers can use linear fractional models for optimizing the expense for construction materials and manpower with ensuring a high quality of works and good meeting of the schedule milestones at the same time.

DETECTION AND CLASSIFICATION OF SUBMICRON SURFACE DEFECTS BASED ON THEIR INTERFERENCE IMAGES USING DEEP LEARNING

Oleksandr Kravchenko — 2025-03-17

An automated method for detecting and classifying submicron surface defects on mirrors used in high-precision optical systems has been developed, utilizing interferometric image analysis and deep learning. This approach replaces manual inspection with a neural network, delivering faster and more objective defect diagnostics, eliminating human bias, and accelerating quality control in serial production of optical components. A synthetic dataset of interferometric images was generated to train the model, simulating scratch-type defects on mirror surfaces through specialized software based on the Linnik interferometer model. The dataset encompasses three surface classes: flat surfaces, single scratches, and multiple scratches. The neural network is built upon MobileNetV2, pre-trained on ImageNet, with fine-tuning of its final blocks to adapt to the task’s specifics. The architecture incorporates GlobalAveragePooling2D for feature compression, Dense layers with ReLU activation and BatchNormalization, Dropout to mitigate overfitting, and a Softmax output layer for classifying the three categories. Data augmentation and soft voting techniques were employed to enhance the model’s generalization ability. Classification accuracy, assessed using the accuracy metric, achieves 96% on the synthetic validation set and 82.7% on real images acquired from a Linnik interferometer. The highest accuracy is observed for flat surfaces, while the lowest occurs for multiple scratches, highlighting challenges posed by real-world conditions such as noise and artifacts. The method proves its practical value for automated diagnostics, with future enhancements tied to improving synthetic data realism-potentially by incorporating modeled noise-and extending the model’s adaptability to additional defect types like indentations or protrusions, thereby broadening its applicability in optical system manufacturing