Stakeholder Requirements Research Articles

Selection Criteria for Requirement Prioritization Techniques for software development: Data Analysis

Requirement analysis is a critical stage in the software development lifecycle that drives project success. Elicitation techniques that are in line with stakeholders' needs, including both functional and non-functional factors, have a significant impact on software results. Finding the best approach to requirement prioritising is difficult, even with the wide range of approaches available. Value-based requirements are carefully woven into particular priority criteria by this study, which pioneers an approach that assesses requirement prioritisation techniques based on four important elements. The work emphasises the computation of full scores for different methods, providing definitive graphical representations that enable comparison assessments. This novel method simplifies requirement-prioritising analysis and selection by providing a structured framework for assessing and selecting the best solution in a particular situation. Through the integration of many elements and alignment with stakeholder requirements, this approach provides a systematic framework designed to improve the effectiveness of requirement prioritising in software development. Its diverse approach tackles the intricacies present in these procedures, ultimately leading to more efficient decision-making in software development projects.

Building Digital Platform for Property Marketing Sales with an Enterprise Architecture Approach

Constructing a digital platform is an essential priority in an era where technology is the cornerstone of success in the real estate marketing and sales sector. Nevertheless, the advancement of these platforms is frequently impeded by obstacles pertaining to integration, security, and scalability, which stem from their inadequate establishment. Existing platforms' incapability to rapidly adapt to shifting market dynamics frequently impedes the development of innovative digital solutions for the real estate industry, which provided the impetus for this study. The fundamental objective of this study is to create a framework capable of accommodating scalable business expansion, enhancing data security, and overcoming integration obstacles. Utilizing Enterprise Architecture principles in the design and implementation of the platform, as well as conducting a comprehensive examination of the current IT infrastructure, stakeholder requirements, and mapping of pertinent business processes, will comprise the research methods. The results of this study are expected to contribute to a wholehearted comprehension of how the Enterprise Architecture approach can function as a resilient framework for the development of effective digital platforms. In the contemporary digital age, this platform is expected to furnish solutions capable of swiftly adjusting and reacting to evolving market dynamics and changes, thereby facilitating the marketing and sales requirements of real estate. Finally, this research endeavors to offer a comprehensive and practical perspective on constructing a robust and flexible digital infrastructure that can effectively cater to the demands of the real estate sector in the current era of digitalization.

Development of FASIH Application for the Badan Pusat Statistisk using Flutter Framework

One of the data collection methods used by the Badan Pusat Statistik (BPS) is Computer Assisted Personal Interviewing (CAPI). Currently, CAPI, known as FASIH, is continuously updated by BPS using the Kotlin programming language, which can run on the Android platform. It is possible that FASIH will be needed in a multiplatform form. However, there is an alternative for multiplatform application development, namely Flutter, which can be used in the development of FASIH. Nevertheless, BPS has not conducted any study on the development of the FASIH application using Flutter, hence the strengths and weaknesses of implementing this technology in FASIH application development remain unknown. Therefore, the author aims to conduct a study on the development of the FASIH application utilizing Flutter. The application development is carried out using the Rapid Application Development (RAD) Prototyping method. The resulting application is tested using black box testing and performance testing using a third-party application, Apptim. The black box testing results indicate that the application meets the functional requirements of stakeholders. In terms of performance, the Kotlin version of FASIH outperforms the Flutter version. However, Flutter has an advantage in accelerating development time. Additionally, concerning user interface development, the Flutter version of the FASIH application can run on multiple platforms. Nevertheless, further integration is required to ensure the proper functioning of the Flutter version of the FASIH application.

Peculiarities of justification of a representative set of requirements for intelligent vehicles

Introduction (problem statement and relevance). The key task of ensuring confidence in motor vehicles with elements of artificial intelligence (AI vehicles) is forming of a representative set of requirements compliance with which ensures the necessary guarantees of AI vehicle functionality and safety.Absence of generally accepted and statutory approaches to justification of the requirements content constrains application of artificial intelligence technologies in motor vehicles and, respectively, impedes creation and improvement of highly automated vehicles. The purpose of the study is to analyze the principles of forming of a representative set of requirements for AI vehicles. Methodology and research methods. The paper uses the principles of qualimetry adapted for the information systems based on machine learning algorithms, it also uses the methods of system analysis, mathematical statistics, combinatorics, set theory and propositional calculus principles. Scientific novelty and results. The approach is formulated to justify the statistically representative set of requirements for AI vehicles, and that allows adequate and accurate assessment of compliance of these systems with the expectations of the customers, developers, suppliers, regulatory authorities and other stakeholders. The structure of such expectations and priorities has been considered taking into account creation and application of AI systems. It is shown that stakeholder requirements apply to both the AI vehicle life cycle processes and the systems per se taking into account particular (given) conditions of their operation. Different significant factors, which variability sets the intended operating conditions, have been considered and conditions of representativeness of test data sets used for AI vehicle trials have been formulated. It is shown that, in order to reduce the dimensionality of space of the intended operating conditions, AI vehicles can be decomposed into separate functional subsystems and then into individual AI algorithms. The ways to perform such functional decomposition have been suggested. Practical significance.The principles of justification of the requirements content offered in the paper can be used in certification testing of artificial intelligence algorithms that are used in highly automated vehicles.

A person-centred approach to help services and commissioners to improve the experience of people who are homeless and secure better sustainability.

Successful implementation of evidence-based services in health and social care depends largely on the fit of the services with the values and priorities of users who are shaping and participating in their delivery and use. When looking at the implementation of innovative specialist hospital discharge schemes for people who are homeless, there is a lack of knowledge on homeless people’s preferences and perspectives on receiving out-of-hospital care.
 In England, the Department of Health and Social Care, Ministry for Housing and Local Government and Ministry of Justice allocated £16 million to further develop and test out hospital care for people experiencing homelessness. The out-of-hospital care model programme is seeking to scale successful hospital discharge models that were shown to be effective and cost-effective in an earlier pilot programme adapting these for new contexts and circumstances post Covid-19. Evaluation of the implementation of the out-of-hospital care model programme is currently underway in 17 local authority test sites across England. A specific component of the larger evaluation is aimed at capturing service users’ preferences for different types of specialist hospital discharge schemes.
 This is done using a flexible health economics tool for measuring choices in health and social care-related settings named discrete choice experiments (DCE). It measures preferences from individual decision-makers over alternative scenarios (or service provisions). Each alternative is described by several attributes (or characteristics) and the choices made between two or more competing scenarios subsequently determine how preferences are influenced by each attribute (eg, which attributes are valued as well as their relative importance). It can also provide a measure of the overall value attached to different alternatives (and identify optimal service provision that meets stakeholder requirements and have the best chance of sustainability in the long term). Hence a DCE survey has been developed to measure people’s preferences for what out-of-hospital care provides in terms of types of location of care, the professional who delivers most of their care, how often they receive care, how long they receive care after hospital discharge and rules about behaviour where they live.
 The attributes (characteristics) and their various levels were informed by our previous research. Webinar discussions regarding what stakeholders value about specialist services were used to validate and refine the attributes and levels. PPI stakeholders were also consulted on the development of the presentation, wording and format of the survey. Efficient experimental design techniques were applied to create the DCE questions. Due to the cognitive load involved in completing the survey, respondents are assisted in the completion of the questionnaire by members of the research team to secure responses from more than 250 people who are homeless. Data will be modelled using logit techniques and results will be ready for presentation in May 2023.
 Information on how service users value specialist hospital discharge schemes for people who are homeless is needed to better inform the implementation and development of the innovative out-of-hospital care model programme currently proposed in England at a national level. Identified learning will be shared at the conference.

Blockchain-based auditing of legal decisions supported by explainable AI and generative AI tools

Generative AI tools powered by Large Language Models (LLMs) have demonstrated advanced capabilities in understanding and articulating legal facts closer to the level of legal practitioners. However, scholars hold contrasting views on the reliability of the reasoning behind a decision derived from LLMs due to its black-box nature. Law firms are vigilant in recognizing the potential risks of violating confidentiality and inappropriate exposure of sensitive legal data through the prompt sent to Generative AI. This research attempts to find an equilibrium between responsible usage and control of human legal professionals over content produced by Generative AI through regular audits. It investigates the potential of Generative AI in drafting correspondence for pre-litigation decisions derived from an eXplainable AI (XAI) algorithm. This research presents an end-to-end process of designing the architecture and methodology for a blockchain-based auditing system. It detects unauthorized alterations of data repositories containing the decisions by an XAI model and automated textual explanation by Generative AI. The automated auditing by blockchain facilitates responsible usage of AI technologies and reduces discrepancies in tracing the accountability of adversarial decisions. It conceptualizes the two algorithms. First, strategic on-chain (within blockchain) and off-chain (outside blockchain) data storage in compliance with the data protection laws and critical requirements of stakeholders in a legal firm. Second, auditing by comparison of the unique signature as Merkle roots of files stored off-chain with their immutable blockchain counterpart. A case study on liability cases under tort law demonstrates the system implementation results.

SCARS: Suturing wounds due to conflicts between non‐functional requirements in autonomous and robotic systems

AbstractIn autonomous and robotic systems, the functional requirements (FRs) and non‐functional requirements (NFRs) are gathered from multiple stakeholders. The different stakeholder requirements are associated with different components of the robotic system and with the contexts in which the system may operate. This aggregation of requirements from different sources (multiple stakeholders) often results in inconsistent or conflicting sets of requirements. Conflicts among NFRs for robotic systems heavily depend on features of actual execution contexts. It is essential to analyze the inconsistencies and conflicts among the requirements in the early planning phase to design the robotic systems in a systematic manner. In this work, we design and experimentally evaluate a framework, called SCARS, providing: (a) a domain‐specific language extending the ROS2 Domain Specific Language (DSL) concepts by considering the different environmental contexts in which the system has to operate, (b) support to analyze their impact on NFRs, and (c) the computation of the optimal degree of NFR satisfaction that can be achieved within different system configurations. The effectiveness of SCARS has been validated on the iRobot Create3 robot using Gazebo simulation.

Developing a climate change inequality health impact assessment for health services.

To develop a Climate Change Inequality Health Impact Assessment (CCIHIA) framework for health services; to provide a systematic process for assessing potential unequal health impacts of climate change on vulnerable and marginalised populations and places; to support effective planning to address these impacts; and to develop contextually appropriate local strategies. Type of program: A collaborative interdisciplinary scoping research project involving two universities and two local health districts (LHDs) in New South Wales (NSW) to develop a CCIHIA framework. This work builds upon the health impact assessment (HIA) approach, which systematically assesses proposals' potential health and equity impacts by involving stakeholders in developing responses. The project involved four main activities: understanding stakeholder requirements; conceptualising climate change vulnerability; considering the role of health services; and integrating findings into a conceptual framework. Stakeholders identified key functions that should be addressed across the framing, process and utility of the CCIHIA framework. The resulting conceptual framework outlines contexts and social stratification, the differential impacts of climate change (including factors influencing unequal impacts) and the health system's position, and also identifies key potential points of intervention. The challenge of addressing the complexity of factors and resulting health impacts is reflected within the CCIHIA framework. While there are many intervention points within this framework for health services to address, many factors influencing unequal impacts are created outside the health sector's direct control. The framework's development process reflected the focus on collaboration and the interdisciplinary nature of climate change response. Ultimately, the CCIHIA framework is an assessment tool and an approach for prioritising inclusive, cross-cutting, multisector working, and problem-solving.

Accounting of State Grants According to International Financial Reporting Standards

Accounting for grant agreements is an important aspect of financial management for a variety of organisations, including not-for-profit, commercial enterprises, and public institutions. Accounting for grants helps to ensure financial openness and transparency in the organisation's activities. This is important to meet the requirements of stakeholders, including donors, government agencies, investors, and the public. The purpose of the study is to identify the areas of grant funds reporting in accordance with international financial reporting standards. The study found that many organisations receive grants from various sources, and this may include funds from governmental organisations and international donors. Accounting for grants helps ensure compliance with the terms and conditions that accompany the grant. Grants accounting helps organisations to effectively manage financial resources provided for specific projects or programmes. This ensures the efficient use of funds and the achievement of planned results. In addition, Grant Accounting allows you to evaluate the results and impact of projects that have been funded by grants. This helps organisations and donors understand how successful their investments have been. In general, grant accounting is important for the financial stability, transparency, and efficiency of grant-receiving organisations. It helps ensure effective management of financial resources and fulfilment of obligations to donors and other stakeholders.

A Novel Model for Dynamic Manufacturing Service Collaboration on Industrial Internet

Industrial Internet enables distributed manufacturing enterprises to efficiently and promptly respond to the requirements of stakeholders using a manufacturing service collaboration chain (MSCC) composed of networked enterprises. However, various dynamic uncertainties may interrupt the MSCC, such as device malfunctions, urgent order insertions, and dynamic logistics, resulting in inexactitude practical applications. This paper proposes a novel reliability-based dynamic manufacturing service collaboration optimization (R-DMSCO) model for uncertain manufacturing collaboration procedures on Industrial Internet. The RDMSCO model reformulates the MSCC reliability in the form of an expectation-standard deviation of uncertain job completion time described by discrete scenarios pertaining to the uncertain perturbation of processing time and logistics time. Subsequently, an enhanced multi-objective artificial bee colony (EMOABC) algorithm that embeds four improvements is intended to address the manufacturing service collaboration optimization (MSCO) problem. The experimental results demonstrate that EMOABC outperforms other typical multi-objective algorithms for MSCO problems. Additionally, the R-DMSCO model can cope with dynamic uncertainties with better robustness and stability than two other effective strategies for dynamic manufacturing service collaboration.

Evaluating Different Power Solutions for Medium Caliber, Counter-Drone Rounds

Drones are a growing part of the battlefield; they are becoming easier to produce and have growing capabilities that make them dangerous. There are many different ways to counter drones and a new medium caliber round may prove to be the best solution. The biggest issue in building this round is finding a power source that is cost-effective while also meeting stakeholder requirements. This study seeks to solve this issue by using a value modeling approach that scores each power source alternative based on metrics that are key to the stakeholder. The first step was to research power source alternatives that met stakeholder requirements. Each alternative was then given a value score based on their respective characteristics. The next step was to create a value versus cost trade space analysis, which provides insight into the tradeoff between value and cost. The analysis shows four feasible alternatives with low costs, and one alternative with a significantly higher cost and value.

ESG transformation as a new management paradigm

Subject. The concept of ESG transformation as a new management paradigm. Objectives. To substantiate the theoretical and methodological aspects of ESG-transformation of control systems, based on the analysis of the main theoretical views and characteristics of this approach. Methods. The theoretical and methodological basis was the author's definition of ESG-transformation of control systems, as well as methods of retrospective factual analysis, generalizations, comparisons and analogies. Results. The article highlights modern challenges in the field of sustainable development and social responsibility, representing ESG transformation as a new management paradigm. It is emphasized that ESG criteria are becoming not only a measure of business sustainability, but also a new approach to management, integrating environmental, social and corporate governance principles into the strategies and decisions of organizations. The key aspects of ESG transformation, its impact on business models, strategic planning and the perception of stakeholders are analyzed. Special attention is paid to the role of ESG in creating sustainable and responsible organizations in the context of the modern socio-ecological paradigm. Conclusions. The study highlights the importance of integrating ESG principles into the strategies and practices of organizations to achieve not only financial success, but also sustainable, responsible development. The results show that ESG transformation not only meets the modern requirements of society and stakeholders, but also provides opportunities for innovation, value creation and strengthening the long-term sustainability of organizations.

Optimal dynamic contract with Knightian uncertainty of ESG rating

In the context of “dual carbon” objectives, ESG investment concept has received wide attention. In this paper, based on stochastic systems and stakeholder theory, we analyze the interest requirements of subject stakeholders and investigate the agency conflict and coordination problems in the system. Combining with the contract theory, we construct a continuous-time principal-agent model with ambiguity aversion under ESG rating Knightian uncertainty. By the method of stochastic calculus in multiple priors, we derive the HJB equation for the optimal dynamic contract value function based on ESG rating Knightian uncertainty under incentive compatibility conditions. Then, for different ESG rating levels, through numerical simulation, we discuss the impact of the Knightian uncertainty degree of ESG rating on the optimal contract. The study shows that as the Knightian uncertainty degree of ESG rating increases, companies are more inclined to green development and the maximum expected return of principals decreases.

Designing digital solutions for sustainability: Navigating conflicting stakeholder requirements with dignity in mind

This teaching case examines the challenges of designing a digital solution to address complex sustainability problems. The case follows the vice president for innovation of a mid-sized European digital agency involved in a project dedicated to designing a digital application that connects climate-conscious investors, banks, and homeowners in need of loans for green house renovations. In this case, students need to navigate stakeholders’ conflicting needs and consider broader aspects of environmental, social, and economic sustainability in the design of digital applications. At the same time, this digital solution should seek to promote stakeholders’ dignity.

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements.

Vertidrome Airside Level of Service: Performance-Based Evaluation of Vertiport Airside Operations

This paper presents the Vertidrome Airside Level of Service (VALoS) framework, a novel performance metric designed to evaluate airside traffic flow operations at vertidromes in the context of Urban Air Mobility (UAM). As the UAM industry rapidly evolves, the need for a comprehensive evaluation framework becomes increasingly important. The VALoS framework provides a performance-based approach to evaluating vertidrome traffic flow performance, considering metrics like average passenger delay, air taxi in-flight delay, and vertidrome punctuality. Unlike existing Level of Service approaches, the VALoS framework unifies the requirements of various stakeholders, the passenger, the air taxi operator, and the vertidrome operator each with their own performance metric and target. It provides a multi-faceted approach covering airside air and ground traffic flows, arrivals and departures, and performance changes during strategic planning and tactical execution phases. The VALoS is evaluated at 15-min intervals while considering changing stakeholder performance targets and operational uncertainties. For the reference use case, the study demonstrates the significant impact of short-term disruptions, while stochastic deviations can be neglected. Higher traffic volumes due to changing demand/capacity ratios result in higher VALoS variability. The VALoS framework, together with a fast-time simulation, provides a versatile method for exploring future vertidrome traffic flows and supporting strategic vertidrome airside planning and integration. This integrated approach is essential for the evolving UAM vertidrome industry; aligning the interests of different stakeholders and promoting sustainable and efficient vertidrome planning and operation.

Common governance model: a way to avoid data segregation between existing trusted research environment.

Trusted Research Environments provide a legitimate basis for data access along with a set of technologies to support implementation of the "five-safes" framework for privacy protection. Lack of standard approaches in achieving compliance with the "five-safes" framework results in a diversity of approaches across different TREs. Data access and analysis across multiple TREs has a range of benefits including improved precision of analysis due to larger sample sizes and broader availability of out-of-sample records, particularly in the study of rare conditions. Knowledge of governance approaches used across UK-TREs is limited. To document key governance features in major UK-TRE contributing to UK wide analysis and to identify elements that would directly facilitate multi TRE collaborations and federated analysis in future. We summarised three main characteristics across 15 major UK-based TREs: 1) data access environment; 2) data access requests and disclosure control procedures; and 3) governance models. We undertook case studies of collaborative analyses conducted in more than one TRE. We identified an array of TREs operating on an equivalent level of governance. We further identify commonly governed TREs with architectural considerations for achieving an equivalent level of information security management system standards to facilitate multi TRE functionality and federated analytics. All 15 UK-TREs allow pooling and analysis of aggregated research outputs only when they have passed human-operated disclosure control checks. Data access requests procedures are unique to each TRE. We also observed a variability in disclosure control procedures across various TREs with no or minimal researcher guidance on best practices for file out request procedures. In 2023, six TREs (40.0%) held ISO 20071 accreditation, while 9 TREs (56.2%) participated in four-nation analyses. Secure analysis of individual-level data from multiple TREs is possible through existing technical solutions but requires development of a well-established governance framework meeting all stakeholder requirements and addressing public and patient concerns. Formation of a standard model could act as the catalyst for evolution of current TREs governance models to a multi TRE ecosystem within the UK and beyond.

Sol-Gel Functionalized Polyurethane Foam-Packed Mini-Column as an Efficient Solid Extractor for the Rapid and Ultra-Trace Detection of Textile Dyes in Water.

Textile dyes widely used in industrial products are known as a major threat to human health and water ecological security. On the other hand, sol gel represents a principal driver of the adoption of dispersive solid-phase microextractors (d-µ SPME) for pollutants residues in water. Thus, the current study reports a new and highly rapid and highly efficient hybrid sol-gel-based sponge polyurethane foam as a dispersive solid-phase microextractor (d-µ-SPME) platform packed mini-column for complete preconcentration and subsequent spectrophotometric detection of eosin Y textile dye in wastewater. The unique porous structure of the prepared sol-gel immobilized polyurethane foams (sol-gel/PUF) has suggested its use for the complete removal of eosin Y dye (EY) from water. In the mini-column, the number (N) of plates, the height equivalent to the theoretical plates (HETP), the critical capacity (CC), and the breakthrough capacities (BC) of the hybrid sol-gel-treated polyurethane foams towards EY dye were determined via the breakthrough capacity curve at various flow rates. Under the optimum condition using the matrix match strategy, the linear range of 0.01-5 µg L-1, LODs and LOQs in the range of 0.006 µg L-1, and 0.01 µg L-1 for wastewater were achieved. The intra-day and inter-day precisions were evaluated at two different concentration levels (0.05 and 5 μg L-1 of dye) on the same day and five distinct days, respectively. The analytical utility of the absorbents packed in pulses and mini-columns to extract and recover EY dye was attained by 98.94%. The column could efficiently remove different dyes from real industrial effluents, and hence the sol-gel/PUF is a good competitor for commercial applications. The findings of this research work have strong potential in the future to be used in selecting the most suitable lightweight growing medium for a green roof based on stakeholder requirements. Therefore, this study has provided a convenient pathway for the preparation of compressible and reusable sponge materials from renewable biomass for efficient removal of EY from the water environment.

Experiences of rural nurses with emergency patient transport in a resource limited setting

BackgroundRegistered nurses are primary care providers during most patient transfers from rural areas. Various local conditions and circumstances impact the provision of nursing care prior to and during transportation. These include clinic staffing, uneven access to functioning equipment and other necessary infrastructure across settings, the wide-ranging clinical need for specialty care, and complex social and interpersonal circumstances that play a role in care-seeking and transport decision-making. This study explored the experiences of nurses with emergency patient transport in rural health facilities in Botswana. MethodA qualitative descriptive approach was used using a semi structured interview. Twenty-six registered nurses from four remote, isolated rural health districts in Botswana participated in this study. Purposive convenience sampling technique was employed. ResultsThe ten main themes under transporter were infringement of scope of practice, inadequate knowledge and skills, distressful practice, restriction from making decisions, challenges with staffing, Ineffective facilities clustering, lack of support from the managers, shortage of technology and tools, non-enabling infrastructure, and transport related tasks. Discussion and ConclusionThe perceived ineffective emergency transfer of patients was associated with work system shortfalls. The work system needs to be balanced and consider the requirements of the various stakeholders involved in the processes for optimal performance of patient transport.

Development of Higher Education of the XXI Century in the World Context in the Face of Global Challenges

The article is devoted to the problem of higher education development in the twenty-first century in the context of global challenges. Since the scientific research did not include an empirical component, theoretical methods of scientific knowledge (analysis, synthesis, abstraction, etc.) became additional components of the methodological toolkit, and the analysis of relevant scientific publications on educational issues for the period of 2019-2023 was taken as a basis. For convenience, the materials were systematized according to the relevant topics: development and implementation of innovative methodology in the educational process; opportunities for integrating digital technologies into education, digitalization; stakeholder requirements for the personality of a specialist and their implementation in the educational process; problems and prospects for the development of the educational sector; transformation of higher education in Ukraine under the influence of a full-scale war. The purpose of the article was to analyze certain aspects of the development of higher education in the twenty-first century. The analysis of publications allowed us to identify visions of ways to solve educational problems and modernize higher education. Particular attention was paid to the peculiarities of the organization of higher education in Ukraine during the war and the factors that will stimulate/hinder the development of post-war Ukrainian higher education. The generalization of information obtained from relevant scientific sources made it possible to model the process of transformation of higher education today, to identify global factors, to point out the incentives and the path of development that the educational sector is currently moving along. The novelty of the proposed research lies in the attempt to systematize existing scientific studies and build a holistic vision of the transformation processes in higher education. The article is aimed at educators, students, educational reformers, and researchers and aims to draw attention to the importance of educational issues.