AI in Software Engineering: Elevating Developer Experience through Codeium and Copilot

Artificial intelligence and recent advances in deep learning architectures, including transformer networks and large language models, change the way people think and act to solve problems. Software engineering, as an increasingly complex process to design, develop, test, deploy, and maintain large-scale software systems for solving real-world challenges, is profoundly affected by many revolutionary artificial intelligence tools in general and machine learning in particular. In this roadmap for artificial intelligence in software engineering, we highlight the recent deep impact of artificial intelligence on software engineering by discussing successful stories of applications of artificial intelligence to classic and new software development challenges. We identify the new challenges that the software engineering community has to address in the coming years to successfully apply artificial intelligence in software engineering, and we share our research roadmap toward the effective use of artificial intelligence in the software engineering profession, while still protecting fundamental human values. We spotlight three main areas that challenge the research in software engineering: the use of generative artificial intelligence and large language models for engineering large software systems, the need of large and unbiased datasets and benchmarks for training and evaluating deep learning and large language models for software engineering, and the need of a new code of digital ethics to apply artificial intelligence in software engineering.

Artificial Intelligence (AI) is increasingly recognized as a transformative force in contemporary diplomacy, fulfilling a dual role as both an operational tool and a central subject of international discourse. On the one hand, AI serves as a powerful instrument within digital diplomacy, enabling states and international organizations to enhance the efficiency, scope, and agility of their diplomatic operations. On the other hand, as the focus of tech diplomacy, AI has become a key topic in international negotiations and norm-setting, shaping the global governance landscape. This study aims to develop a comprehensive conceptual framework for analyzing the dynamic intersection between diplomatic practice and AI, with a particular emphasis on how technological innovation is reconfiguring international policy, governance mechanisms, and patterns of global economic growth. Through a comparative theoretical analysis, this research investigates the multifaceted impact of AI on diplomatic communication, conflict prevention, and global coordination. It further explores how AI influences the evolution of international norms, the design of institutional structures, and the dynamics of geopolitical competition. In the context of digital diplomacy, AI-driven tools such as chatbots for consular assistance, predictive analytics for real-time crisis monitoring, and advanced disinformation detection systems—exemplified by Ukraine’s Osavul and NATO’s digital initiatives—significantly enhance the speed, reach, and responsiveness of diplomatic activities. These innovations not only streamline traditional diplomatic processes but also create new opportunities for creative statecraft and international engagement. Conversely, in the domain of tech diplomacy, AI has emerged as a central subject of multilateral negotiations and regulatory discussions. Conceptual frameworks presented in this study underscore AI’s dual function as both a geopolitical lever and a catalyst for international cooperation, necessitating robust regulatory alignment and the establishment of export controls on advanced AI technologies. The study identifies several critical challenges associated with the integration of AI into diplomacy, most notably the urgent need for global consensus on AI governance and the effective management of its strategic risks. The complexity of these challenges is compounded by the rapid pace of technological development and the divergent interests of state and non-state actors. In response, public-private partnerships (PPPs) are emerging as vital enablers, fostering collaboration across sectors, bridging knowledge gaps, and mobilizing resources for the responsible and inclusive development of AI on a global scale. PPPs play a crucial role in advancing standardization, policy harmonization, and the equitable distribution of AI benefits, thereby reinforcing the capacity of states and international organizations to address emerging challenges. Ultimately, AI is not only redefining the functions and practices of diplomacy but also transforming the broader landscape of international relations. By necessitating new frameworks for cooperation, regulation, and ethical oversight, AI is reshaping the foundations of global order and stability. The findings of this study highlight the strategic importance of aligning AI development with core values of global stability, equity, and shared prosperity. As AI continues to evolve, diplomatic efforts must adapt to ensure that technological progress serves the collective interests of the international community, fostering a future in which AI-driven diplomacy contributes to peace, security, and sustainable development.

Artificial Intelligence (AI) is rapidly redefining the software engineering landscape.It enhances development speed, accuracy, and scalability across various phases of the Software Development Life Cycle (SDLC).From automating mundane coding tasks to intelligent debugging and project optimization, AI empowers developers to focus on innovation.Recent advancements in generative AI, machine learning, and natural language processing have introduced tools that assist with code generation, automated software testing, bug detection, and agile management.This research synthesizes insights from eleven notable IEEEindexed papers published between 2013 and 2024.Each of these papers explores different dimensions of AI's influence in software development-from secure DevOps and test automation to reuse of software components and explainable AI systems.Our findings confirm that the integration of AI into software processes boosts productivity, reduces error rates, and encourages modular development.However, certain limitations like lack of transparency, ethical concerns, and reliance on historical training data persist.This paper aims to present a well-rounded perspective on the current role and future potential of AI in software engineering.Furthermore, the adoption of AI in software engineering is not just limited to automation but is also influencing architectural decisions and continuous integration practices.By integrating AI into DevOps pipelines, development teams gain real-time insights, predictive analytics, and adaptive responses to changing requirements.The role of AI in enhancing developer productivity, improving software quality, and enabling faster time-to-market is becoming increasingly critical in today's competitive and agile-driven software industry

Urgency of the research of artificial intelligence research in a digital society presents numerous causal changes, among them: space research, biotechnology, nanotechnology, digital technology, etc. The implications for human beings and human civilisation are significant as they affect a number of already existing contemporary issues such as economics, politics, ethics, law, psychology, conflictology, sociology, and ecology. Anthropology has to return to the existence of human beings in a society of artificial intelligence, consciousness and even the physical body. The study conceptualises artificial intelligence as a factor of ecosystem growth in the context of technological, ontological, and anthropological dimensions. The research objectives: 1) to theorise artificial intelligence as a potential and resource for use in all sectors of the socioeconomic sphere in the context of technological dimensions; 2) to clarify the convergence of artificial intelligence and its main components in the context of ontological dimensions; 3) to reveal the role of artificial intelligence in enhancing the digitalisation of society and humans in the context of anthropological dimensions. In the research we are guided by the works of scholars who have studied and are studying the problems of artificial intelligence as a complex social, economic, and cultural phenomenon: R. Andriukaitienė, N. Boström, A. Vance, P. Diamandis, S. Kotler, P. Dickson, L. Kai-Fu, and K. Kell, K. Kell, and others. Research methodology. The theory of artificial intelligence is a complex multidisciplinary science, which is located at the junction of economics, sociology, computer science, mathematics, psychology. Summarizing the processes of interdisciplinary interaction of artificial intelligence, we note that its study requires an interdisciplinary approach based on the synthesis of scientific knowledge, as revealing the content and principles of functioning of a complex invariant system of artificial intelligence. To analyze the modern model of artificial intelligence, it is necessary to apply a systematic institutional-evolutionary approach. By analysing different methodological approaches, it is necessary to acknowledge the presence of an anthropogenic component, as the methodology of scientific research is based on the method of information-anthropogenic analysis based on the fundamental position that no artificial intelligence processes are possible without information/data as an object and human as a subject of cognition. In addition, the following methods were used: induction, deduction, sublimation, synergetics, evolutionary historicism, modelling, and forecasting. The result of the research. Artificial Intelligence (AI) is a broad term used to define technologies/ engineering systems that emulate human intelligence, linking to neurophysiology, robotics, psychology (pattern recognition, modelling of psychological processes), transhumanism, cybernetics (computing power to find patterns in large data sets). Artificial Intelligence has been defined as a software-engineered system that is capable of influencing the environment with varying degrees of autonomy, producing results (predictions, recommendations, decisions) for a specific set of goals for the digitalisation of society. On the other hand, artificial intelligence causes many problems that are seen as cyber/virtual addiction, loss of reality, increased surveillance, loss of protocol control, privacy issues, cybersecurity, etc. The convergence of large data with artificial intelligence has become the single most important development shaping the future of social institutions in the digital society and the prospects for realising potential opportunities. Thanks to the latest digital technologies, advances in software engineering, and evolving artificial intelligence, there are both new perspectives for solving the most complex problems of modern civilization and new challenges in the technological, ontological, and anthropological dimensions.

This study explores the transformative role of Artificial Intelligence (AI) within the context of Regional Innovation Ecosystems (RIVs), Smart Specialisation Strategies (RIS3), and the European Union’s Mission on Adaptation to Climate Change. Building upon ten empirical cases from nine countries in the Baltic Sea region, the research examines AI’s potential to enhance sustainable development, climate resilience, and inter-regional cooperation.While the literature on regional innovation emphasizes governance models, stakeholder engagement, and impact assessment, the specific contributions and limitations of AI within these frameworks remain underexplored. This study aims to fill that gap by analyzing the integration of AI across various components of regional development strategies.AI offers substantial benefits in climate adaptation through enhanced data processing capabilities. It improves climate modeling and forecasting by analyzing vast datasets from satellites, sensors, and historical records, enabling more accurate predictions of extreme weather events and strengthening early warning systems. AI supports the optimization of critical resources such as water, energy, and land, and informs the development of adaptive infrastructure including smart energy grids. It also aids in ecosystem conservation and enhances carbon capture through predictive modeling.In the domain of regional collaboration, AI facilitates partner identification and strategic matchmaking by analyzing regional strengths, competencies, and innovation needs. It supports multilingual knowledge-sharing platforms and provides real-time translation, thus lowering communication barriers and improving cross-border collaboration. AI-driven tools also enhance the Entrepreneurial Discovery Process (EDP) by delivering market trend analysis, identifying niche areas for specialization, and simulating policy outcomes to guide strategic decisions.When embedded in participatory processes, AI augments human-centric approaches by analyzing qualitative data from stakeholder workshops, identifying dominant themes, and offering real-time facilitation support. Furthermore, AI can support the creation of targeted communication materials, helping tailor regional development messages to different audiences.The benefits of AI integration include increased speed and efficiency in innovation processes, greater accuracy in modeling and assessments, enhanced foresight through pattern recognition, optimized resource allocation, and scalable solutions that can address both local and global challenges. AI also helps navigate the complexity inherent in multi-stakeholder ecosystems and climate governance.However, several disadvantages merit attention. High initial costs for infrastructure and skilled personnel can impede adoption, especially in less developed regions. The reliability of AI depends heavily on data quality; biased or incomplete data can lead to flawed outputs. Ethical risks include algorithmic bias, lack of transparency, and the “black box” nature of many AI systems. These concerns call for robust oversight and inclusive governance mechanisms. Additionally, AI cannot replicate human intuition, creativity, or value-based judgment, which are essential in participatory and strategic processes. Risks such as job displacement, cybersecurity threats, and the environmental impact of energy-intensive AI systems also raise critical concerns. Finally, there is a risk of widening the digital divide between technologically advanced and under-resourced regions.In conclusion, AI holds significant potential to accelerate sustainable regional innovation, smart specialisation, and climate adaptation. However, realizing these benefits requires a careful balance between technological advancement and social responsibility. Human agency, ethical oversight, and inclusive governance must remain central to ensure that AI contributes to equitable and resilient regional futures.

Software Engineering: A Methodical Approach (Second Edition) provides a comprehensive, but concise introduction to software engineering. It adopts a methodical approach to solving software engineering problems, proven over several years of teaching, with outstanding results. The book covers concepts, principles, design, construction, implementation, and management issues of software engineering. Each chapter is organized systematically into brief, reader-friendly sections, with itemization of the important points to be remembered. Diagrams and illustrations also sum up the salient points to enhance learning. Additionally, the book includes the author’s original methodologies that add clarity and creativity to the software engineering experience. New in the Second Edition are chapters on software engineering projects, management support systems, software engineering frameworks and patterns as a significant building block for the design and construction of contemporary software systems, and emerging software engineering frontiers. The text starts with an introduction of software engineering and the role of the software engineer. The following chapters examine in-depth software analysis, design, development, implementation, and management. Covering object-oriented methodologies and the principles of object-oriented information engineering, the book reinforces an object-oriented approach to the early phases of the software development life cycle. It covers various diagramming techniques and emphasizes object classification and object behavior. The text features comprehensive treatments of: Project management aids that are commonly used in software engineering An overview of the software design phase, including a discussion of the software design process, design strategies, architectural design, interface design, database design, and design and development standards User interface design Operations design Design considerations including system catalog, product documentation, user message management, design for real-time software, design for reuse, system security, and the agile effect Human resource management from a software engineering perspective Software economics Software implementation issues that range from operating environments to the marketing of software Software maintenance, legacy systems, and re-engineering This textbook can be used as a one-semester or two-semester course in software engineering, augmented with an appropriate CASE or RAD tool. It emphasizes a practical, methodical approach to software engineering, avoiding an overkill of theoretical calculations where possible. The primary objective is to help students gain a solid grasp of the activities in the software development life cycle to be confident about taking on new software engineering projects.

Artificial intelligence (AI) is transforming the global advertising industry, altering creative processes, job roles, and workforce structures. While AI-driven tools such as ChatGPT and DALL-E enhance efficiency and campaign personalisation, concerns about job displacement and the ethical implications of AI-generated content persist. In Nigeria, the digital transformation of advertising is uneven, with small and medium enterprises relying on manual methods while a select few access advanced AI tools. Advertising students, as future industry professionals, are particularly impacted by these changes. Their career aspirations may be shaped by how they perceive AI’s influence on advertising operations, creativity, and job security. This study explores Nigerian advertising students’ attitudes toward AI in campaign development, its effect on job prospects, and whether AI is viewed as an opportunity or a threat. Using qualitative semi-structured interviews with 19 students across different educational levels, the research uncovers four key themes: concerns about job security, perceptions of AI’s benefits and limitations, evolving industry skill demands, and AI’s role in human creativity. Findings reveal a dual perspective—some students fear AI’s impact on entry-level jobs, while others see AI as a tool for innovation and efficiency. Ethical concerns, such as AI-generated misinformation and cultural insensitivity, were also highlighted. The study contributes to debates on AI’s role in advertising, emphasising the need for human-AI collaboration, regulatory oversight, and adaptive skill development. By addressing these concerns, the advertising industry can ensure AI serves as an enhancer rather than a disruptor of human creativity and career opportunities.

Abstract. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.

Abstract. Problem. The rapid advancement of artificial intelligence (AI) has significantly impacted various domains, particularly education. AI-driven tools are increasingly being integrated into teaching methodologies, assessment frameworks, and curriculum design. However, despite the growing research interest in Artificial Intelligence in Education (AIED), the field remains fragmented, lacking a comprehensive analysis of dominant research themes and emerging trends. Understanding these trends is crucial for optimizing AI applications in education and shaping future developments in the field. Goal. The primary goal of this study is to analyze the current landscape of AIED research by identifying key thematic clusters, evaluating dominant research directions, and predicting future trajectories in AI-driven education. Methodology. This study employs network analysis, topic modeling, and global research trend evaluation to analyze scientific publications in AIED. The Latent Dirichlet Allocation (LDA) algorithm is applied to classify research articles into thematic categories, enabling the identification of primary topics shaping the field. Data is sourced from the Web of Science database, and hierarchical clustering techniques are used to determine semantic similarities between thematic models. Results. The analysis reveals five major research clusters in AIED, including AI applications in medical education, machine learning in education, adaptive learning technologies, large language models in learning, and student-centered AI-driven educational strategies. The study identifies increasing trends in the adoption of generative AI and chatbot-based learning support, while traditional AI-driven assessment methodologies show moderate growth. The results also highlight strong interconnections between AI-driven personalization and intelligent tutoring systems, emphasizing the shift toward adaptive and student-centric learning environments. Originality. This research provides a structured and systematic analysis of AIED trends, leveraging advanced topic modeling techniques. Unlike previous studies focusing on isolated AI applications in education, this study presents a holistic view of thematic structures in AIED research, offering a quantitative and data-driven approach to understanding the evolution of the field. Practical Value. The findings can serve as a strategic reference for educators, policymakers, and AI developers in shaping future educational technologies. By identifying key thematic directions, this research supports evidence-based decision-making for integrating AI into educational systems. The results can also guide further research in AIED, helping scholars explore emerging AI-driven teaching methodologies, adaptive learning models, and ethical considerations in AI-powered education.

With the increasing complexities and uncertainties, the integration of artificial intelligence has become paramount as the utilization of digital technologies has transformed the management practices across industries, especially in the domain of megaprojects. The paper analyzes the influence of integrating artificial intelligence (AI)- based knowledge management initiatives on the decision-making process of leadership in large-scale initiatives. As the supply chains of large-scale initiatives face complex challenges, leveraging artificial intelligence-based knowledge management initiatives has become essential to enhance resilience and operational efficiency. The qualitative data analysis has shown how AI-driven tools can significantly improve the data analysis in real time, ultimately facilitating knowledge management and enhancing the predictive insights shaping and devising strategic decisions. The AI adoption helps in fostering collaboration through knowledge sharing among the stakeholders, facilitating and promoting innovation and sustainability throughout the lifecycle of the project. Moreover, the AI utilization can deal with the upcoming challenges and uncertainties, helping in informed choices that align with the strategic objectives. The paper discusses the challenges in adopting the AI-based knowledge management initiatives. Leadership support can be marked vital as a clear support with visionary guidance is necessary to gain traction. The study investigates the role of leadership support in the AI-based KM initiatives for the success of the project. Leadership support plays an important role in the implementation of AI by creating a culture of knowledge sharing and embracing innovation and change. Furthermore, strong leadership assists in utilizing the resources, sustainably, and allocates them in the training and development of the human resource to equip them with the necessary knowledge with the help of AI. The qualitative interviews from the industry professionals reflected the positive influence of this step, as it not only empowered the employees but also mitigated the resistance to change. In summary, the paper reflects preliminary findings from an ongoing study that there is a need for leadership support to implement AI-based KM initiatives in megaprojects by articulating a clear vision, securing resources, addressing challenges, and maintaining and sustaining competitive advantage in megaprojects. This supportive framework ultimately enhances the likelihood of achieving project success and driving sustainable growth.

Artificial intelligence (AI) is transforming cardiovascular imaging (CVI), enhancing accuracy, efficiency, and diagnostic capability across echocardiography (Echo), cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR). In Echo, AI improves image acquisition, segmentation, quantification of chamber function, and detection of wall motion abnormalities, supporting diagnosis and prognosis in various diseases. Automated two-dimensional and three-dimensional (3D) analysis allows rapid, reproducible assessments of ventricular volumes and EF. In valvular heart disease, AI assists in measurement, procedural planning, and integration with 3D printing. CCT benefits from AI at every workflow stage, from image acquisition to disease assessment. AI optimizes scanning protocols, reduces radiation exposure, and enhances coronary artery calcium scoring, plaque analysis, and ischemia evaluation. Algorithms enable rapid segmentation and functional assessment, while ongoing studies support its utility in risk prediction and plaque characterization. In CMR, AI accelerates acquisition, reduces artifacts, and automates segmentation and tissue characterization. Deep learning (DL) models accurately detect fibrosis, scar, and functional parameters, positively influencing prognosis prediction in every cardiac disease. AI-driven tools also streamline report generation, enhance Telemedicine workflow, and guide less experienced users in image acquisition. Despite these advances, challenges remain. Robust and diverse datasets, explainable AI models, regulatory approvals, and ethical considerations are critical for safe and widespread adoption. AI’s “black box” nature hinders clinician trust, making interpretability essential. As these barriers are addressed, AI is expected to become an essential tool in every aspect of CVI, enabling personalized medicine, improving patient care, and optimizing clinical workflows in the coming decades.

The unprecedented and exponential advancement of artificial intelligence (AI) has permeated virtually every domain of human activity, outpacing our ability to fully comprehend or regulate its long-term implications. Despite the growing ubiquity of AI systems, regulatory, legal, and ethical frameworks have lagged significantly behind, leaving society exposed to both immense opportunities and unforeseen risks (Bostrom, 2014; Floridi & Cowls, 2019). Among the many areas affected, education - particularly foreign language acquisition - has experienced transformative changes. AI-driven tools now offer enhanced learner autonomy, greater interactivity, and highly personalized instruction, often increasing engagement, enjoyment, and self-efficacy among students (Godwin-Jones, 2020; Wei et al., 2023). However, this technological integration is not without its pitfalls. Emerging concerns include cognitive overreliance on AI-mediated assistance, diminished metacognitive reflection, and variable effects on students’ willingness to engage in authentic human communication (Yao & Liu, 2025; Liu et al., 2023). This article aims to critically examine this evolving educational landscape by weighing the motivational benefits and drawbacks of AI in foreign language learning and by highlighting the urgent need to redefine instructional strategies that support sustainable, human-centered motivation in the AI era.

This article presents an epistemological reading of knowledge evolution in software engineering (SE) both within a software project and into SE theoretical frameworks principally modeling languages and software development life cycles (SDLC). The article envisages SE as an artificial science and notably points to the use of iterative development as a more adequate framework for the enterprise applications. Iterative development has become popular in SE since it allows a more efficient knowledge acquisition process especially in user intensive applications by continuous organizational modeling and requirements acquisition, early implementation and testing, modularity,… SE is by nature a human activity: analysts, designers, developers and other project managers confront their visions of the software system they are building with users’ requirements. The study of software projects’ actors and stakeholders using Simon’s bounded rationality points to the use of an iterative development life cycle. The later, indeed, allows to better apprehend their rationality. Popper’s knowledge growth principle could at first seem suited for the analysis of the knowledge evolution in the SE field. However, this epistemology is better adapted to purely hard sciences as physics than to SE which also takes roots in human activities and by the way in social sciences. Consequently, we will nuance the vision using Lakatosian epistemology notably using his falsification principle criticism on SE as an evolving science. Finally the authors will point to adaptive rationality for a lecture of SE theorists and researchers’ rationality.

Artificial Intelligence (AI) is becoming a fundamental component of software engineering, both in terms of using AI applications in software engineering (AI for SE) and using software engineering to develop AI systems (SE for AI).The SWEBOK highlights relevant AI knowledge areas, but in practice, AI impacts all aspects of software engineering.As a result, software engineering education needs to take account not only of a set of knowledge areas but also a foundational set of AI literacies that inform students about the wider and deeper implications of AI systems.This article addresses this challenge by mapping knowledge areas from the SWEBOK to the Scaffolded AI Literacy (SAIL) Framework.Using some example mappings from knowledge areas to AI literacies, followed by illustrative examples of teaching scenarios, it explores how SE knowledge areas can be supported and enhanced by developing AI literacies.

This study explores the role of artificial intelligence (AI) in K–12 student assessment practices, focusing on educators’ use of AI tools. Through content analysis of active Facebook groups dedicated to AI in education, the authors examined how educators integrate AI into assessment across various grade levels and subjects. Using the Technology Integration Model, they analysed posts to evaluate the use of AI across subjects and grade levels. The findings revealed a progression from initial interest and experimentation with AI to more advanced, adaptive uses in assessment creation and grading. Educators reported positive experiences with AI-driven tools, especially in automating tasks and gamifying assessments. However, challenges such as ensuring reliable, context-specific feedback persist. Overall, the research highlights AI’s potential to enhance student-centred assessment practices and provides insights for educators, administrators and policymakers on AI’s evolving role in education.