The role of artificial intelligence in developing engineering project management

Background: The evolving business environment and technological advancements in the UK construction industry, particularly Artificial Intelligence (AI), are reshaping project management processes, including risk management (RM). As the industry continues to evolve, project managers need to develop AI competencies to effectively mitigate emerging risks. Background: The UK construction industry is undergoing a transformation, driven by technological advancements, particularly the integration of Artificial Intelligence (AI). This shift is reshaping traditional project management practices, including risk management (RM). With the increasing complexity of construction projects and evolving risks, it is essential for project managers to develop AI competencies to effectively navigate and mitigate emerging risks. Aim: This review aimed to evaluate the significance of AI competence for project managers and its impact on risk management practices within the UK construction industry. Methods: Using diverse databases including ProQuest, MEDLINE, and IEEE Xplore, a comprehensive literature review was conducted focused on studies from 2010 to the present related to AI competencies for project managers and risk management in the UK construction industry. The articles were initially screened based on relevant and specific keywords including Boolean operators and followed by full-text review to determine final choice articles used for this review. The findings were summarized using the narrative synthesis approach to report the findings in line with the review objectives. Results: The findings of this review highlight the critical role of AI competence among project managers in enhancing risk management (RM) practices within the UK construction industry. While AI integration improves decision-making, efficiency, and accuracy while reducing human error, the effectiveness of these benefits hinges on project managers’ ability to competently utilize AI tools. The findings were thematically categorized into: (1) AI in the construction industry, (2) AI integration in RM practices, (3) benefits and challenges of AI adoption, and (4) assessing project managers' AI competencies. Case studies, such as IBM’s Watson Explorer and RiskLens, underscore the necessity of AI competence for optimizing RM strategies. Conclusion: AI is revolutionizing RM practices by offering data-driven insights and predictive risk analysis within the UK construction industry. To harness AI's full potential, project managers must develop AI-related competencies. However, for successful adoption of AI in construction, addressing challenges related to competency, ethics, and cost is crucial. AI competencies development among project managers is key to maximizing AI’s benefits, ensuring effective risk management and successful projects.

With the rapid development of artificial intelligence technology, the field of engineering project management is undergoing profound changes. The traditional teaching mode of engineering project management courses can nolonger meet the industry's demand for versatile talents, making teaching reform imperative. Focusing on the integration of artificial intelligence (AI) with the teaching of the “Engineering Project Management” course, this paper explores the impacts of the development of AI technology on the reconstruction of teaching content, the innovation of teaching methods, and the evaluation system, etc., in combination with the characteristics of the course. It also analyzes the problems existing in the integration of AI into teaching and the corresponding.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Aims: This study aims to evaluate the impact of artificial intelligence on project management processes, with a focus on resource forecasting. It further identifies key implementation challenges and provides strategic recommendations for effective AI integration. Study design: Analytical research based on secondary data and simulation modeling. Place and Duration of Study: The research was conducted through analytical evaluation of global scientific publications and applied modeling between January 2024 and February 2025. Methodology: The study applied an analysis of scientific literature to identify current trends in the use of artificial intelligence, a structural-logical method for generalizing forecasting processes, and machine learning-based simulation modeling to evaluate AI capabilities in project management. Comparative analysis and synthesis methods were used to develop practical recommendations, considering organizational, technical, and economic constraints. Results: The study revealed that artificial intelligence improves the accuracy of project resource forecasting, reduces the risk of exceeding budgets, and enhances adaptability to change. In particular, the use of machine learning reduced forecasting errors by up to 17% compared to traditional methods. Key implementation challenges were identified, including poor data quality, high implementation costs, and insufficient staff competencies. A phased approach to AI integration is proposed, including pilot project deployment, reliable data preparation, and targeted staff training. Conclusion: Artificial intelligence plays a critical role in enhancing the effectiveness of strategic project management by enabling more accurate forecasting, optimized resource allocation, and timely adaptation. Future research should focus on improving algorithmic accuracy, developing industry-specific AI models, and creating implementation standards to ensure long-term project sustainability and resilience. This article is important for the scientific community as it addresses a critical challenge in modern project management - the integration of artificial intelligence into resource forecasting. The review systematizes recent interdisciplinary findings and supports the development of advanced methodologies at the intersection of AI and strategic planning. It contributes to bridging theoretical knowledge and practical applications across various industries. The results may serve as a foundation for further research and innovation in digital project management.

Business diversification enables firms to achieve revenues but comes with increased risks when compared to operating in a single industry. Business diversification risks range from wrong investments to a total failure to compete with market competition and complete withdrawal from those markets. Business leaders invest in finding means to successfully diversify their business to achieve business sustainability and success. Grounded in the modern portfolio theory, the purpose of this chapter is to explore if integrating engineering management, project management, and artificial intelligence may sustain projects across diversified business portfolios. Data were collected from a review of previous literature and cases of successful integrations of engineering and project management and artificial intelligence from well-known brands globally. A key recommendation is to invest in such an integration. The integration may allow business leaders to sustain projects across diversified business portfolios, maintain operations, and contribute to communities' stability and economic growth.

The increasing complexity of international engineering projects demands a structured approach to ensure both safety and efficiency across diverse geographical, regulatory, and operational environments. This paper presents a generic framework for managing these challenges by integrating key concepts and strategic approaches. The framework emphasizes the importance of proactive risk management, regulatory compliance, and cross-cultural collaboration to enhance safety protocols and operational efficiency. It focuses on leveraging project management methodologies such as Agile, Lean, and Six Sigma to streamline processes, reduce waste, and improve decision-making in international contexts. Additionally, the framework advocates for the use of advanced technologies, including digital twins, predictive analytics, and real-time monitoring systems, to ensure timely identification of potential risks and optimize project outcomes. The framework also explores the significance of developing a strong safety culture within multinational teams through consistent training, clear communication, and adherence to international safety standards like ISO 45001. Strategic stakeholder engagement is highlighted as critical to aligning project goals with local regulatory requirements and fostering collaborative problem-solving. The paper reviews case studies of successful international engineering projects, illustrating the application of these concepts in different sectors such as construction, energy, and infrastructure development. By adopting this integrative framework, engineering firms can achieve higher levels of safety and operational efficiency, minimizing delays and cost overruns while ensuring regulatory compliance. The paper concludes by discussing future trends, including the growing role of automation, artificial intelligence, and sustainability considerations in engineering project management. Ultimately, this framework offers a comprehensive guide for professionals aiming to navigate the complexities of international engineering projects while prioritizing safety and efficiency.

How to evaluate the performances of an engineering project management and what’s the key factors were important and hot topics in theories of practice of projects management field. This paper analyzed engineering project management in view of teams, summarized factors of engineering project, performances of successful engineering project, and built an engineering project management evaluation model. Then analyzed the model with statistics and structural equation modeling, and extracted some key factors or performances for engineering project management.

The integration of Artificial Intelligence (AI) in project management is reshaping managerial roles, organizational maturity, and required technical competencies. This study examines the relationships between key constructs influencing AI adoption in project management: Technical Prerequisites (TP), Digital Knowledge of the Project Manager (DGK), Changes in the Project Manager’s Job due to AI-driven Digital Transformation (CDT), and AI-driven Digital Maturity of the Organization (AIM). The data collected from 186 project management professionals across eight countries (Romania, Belgium, Austria, Germany, the Netherlands, Italy, the United Kingdom, and France) and was analyzed using Structural Equation Modeling (SEM) with SmartPLS 4.0. The results of the research demonstrate how important technical preparedness and digital expertise are to facilitating AI-driven changes in project-based settings. Furthermore, this paper offers a framework for businesses looking to improve digital maturity through project management techniques, as well as theoretical and practical insights into the changing capabilities needed for AI adoption.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Artificial Intelligence (AI) has emerged as a disruptive force, transforming industries and revolutionizing business processes. Among the domains significantly impacted, project management holds immense potential for transformation. AI’s integration promises intelligent automation, data-driven decision-making, and predictive capabilities, addressing challenges in traditional project management methodologies. Successful AI implementations have revolutionized project management, improving forecasting, resource allocation, and risk assessment. Despite the benefits, challenges hinder AI adoption, including data quality, investment uncertainties, workforce readiness, and change management. However, AI’s potential to address gaps in project management is substantial, enabling data-driven decisions, real-time monitoring, resource optimization, and enhanced agility. This research examines AI’s evolution, successful implementations, challenges, and potential implications, providing insights for effective AI integration in project management. Key Takeaways: • Project management is one domain that stands out as highly impacted by artificial intelligence, offering immense potential for transformation through AI integration. • AI-driven solutions in project management promise intelligent automation, data-driven decision-making, and enhanced predictive capabilities, addressing traditional methodological limitations. • Despite challenges, AI holds substantial potential to address existing gaps in project management practices.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance.

Artificial General Intelligence (AGI) is set to profoundly impact the traditional upstream geoenergy industry (i.e., geothermal energy, oil and gas industry) by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the upstream geoenergy landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the upstream geoenergy industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream geoenergy industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.

Artificial Intelligence (AI) is rapidly transforming the disciplines of project management and software testing, evolving them from manual, reactive processes into intelligent, predictive ecosystems. By harnessing machine learning, natural language processing (NLP), and automation, AI enhances decision-making, risk forecasting, and resource optimization. This paper explores how AI improves defect detection, automates test generation, and strengthens project execution. It also addresses integration challenges, ethical considerations, and future trends toward autonomous systems that continuously learn and adapt. As organizations pursue agility and precision, AI emerges not merely as a tool—but as a strategic enabler of intelligent delivery [1][2]. Keywords Artificial Intelligence (AI), Project Management, Software Testing, Predictive Analytics, Machine Learning, Natural Language Processing (NLP), Automation, Decision Support Systems, Risk Management, Resource Optimization, Digital Transformation, Autonomous Systems.

The healthcare system in Bihar faces numerous challenges, including inadequate medical infrastructure, a shortage of healthcare professionals, and limited access to healthcare facilities, especially in rural areas. The advent of the Internet of Things (IoT) presents a transformative opportunity to address these challenges by enabling real-time health monitoring, remote consultations, and efficient resource allocation. IoT in healthcare involves the integration of smart devices, wearable sensors, cloud computing, and artificial intelligence (AI) to enhance patient monitoring and healthcare delivery. IoT-enabled devices can collect real-time health data, transmit it to medical professionals, and facilitate timely interventions. AI-driven analytics can predict and detect diseases at an early stage, enabling timely medical interventions. IoT-based Smart Health Monitoring Systems have the potential to revolutionize healthcare in Bihar by improving accessibility, efficiency, and quality of care. I am going to discuss the key challenges, including technological infrastructure, data security and privacy, and integration with existing healthcare frameworks. This paper explores how IoT-based Smart Health Monitoring Systems (SHMS) can revolutionize healthcare in Bihar, examines the associated challenges, and identifies potential opportunities. This paper also provides strategic recommendations for policymakers to ensure successful deployment and adoption of IoT-driven healthcare solutions.

Artificial Intelligence (AI) is revolutionizing road safety by minimizing human errors, optimizing traffic flow, and enhancing vehicle automation. AI-driven technologies, including Advanced Driver Assistance Systems (ADAS), autonomous vehicles, predictive analytics, and smart traffic management, contribute accidents and to reducing improving transportation efficiency. By leveraging real-time data from sensors, cameras, and Vehicle-to-Everything (V2X) communication, AI enables proactive hazard detection, adaptive decision-making and efficient resource allocation. However, challenges such as data privacy, system reliability, and legal accountability remain key concerns in AI-driven transportation. Continuous advancements in AI algorithms, machine learning, and regulatory frameworks are addressing these challenges, ensuring safer and more effective road systems. As AI integration in transportation expands, it has the potential to significantly reduce traffic related fatalities and create a safer, smarter and more sustainable driving environment worldwide. This paper explores AI's impact on road safety, highlighting its benefits, challenges, and future potential.