Using Artificial Intelligence for Resource Forecasting in Strategic 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.

As observed from the reviewed literature, the execution of research and development (R and D) projects in Nigeria is challenged by many social, political and economic factors interacting in complex and dynamic ways. This interaction is reflected in the history of science and technology in Nigeria, its culture, legal systems, institutional frameworks and social capital. The economic and social development of Nigeria is critically dependent upon the ability to establish a competitive, productive and efficient industrial sector built on a strong technology base. This implies that her natural resources must be developed and utilised as inputs to industrial production and as direct products to improve the life of the population. Public research organisations (PROs) are considered to be critical to the survival of industries and to the achievement of self-reliance through the use of locally available raw materials. This thesis explores the application of strategic project management (SPM) practices in the execution of R and D projects in PROs in Nigeria. The research employed a mixed method of quantitative and qualitative research in order to capture the experiences of project management practitioners with SPM practices in PROs in Nigeria. Although areas of variations in practice were identified, the major findings highlighted that there was a gap in the practical implementation of SPM. The findings also revealed key determinants associated with the implementation of SPM and the factors that affected its application in PROs in Nigeria. The research findings were further synthesised into a framework, capturing ten key dimensions that must be taken into account in the execution of research and development projects. The determinants include having a project-based organisational structure, top management involvement in the project execution, strategic project leadership style, the appointment of a project team, the project team's competence, project alignment with organisational strategy, project prioritisation and selection, maximising R and D strategy, the project management process, and the SPM process. The framework validation was a follow-up discussion, which was conducted with project management practitioners in the selected research organisations in Nigeria. Reflecting on their experiences in the management of R and D projects, the participants acknowledged that the proposed SPM framework and its ten key variables were fundamental to the effective execution of R and D projects in PROs in Nigeria. It was concluded that the use of such a framework would highlight areas that needed to be addressed in order to achieve effective execution of R and D projects in these organisations.

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.

In the landscape of Small and Medium-sized Enterprises (SMEs) within the manufacturing sector, achieving sustainable growth amidst increasing competition and evolving market demands presents a significant challenge. This abstract delves into the pivotal role of digital transformation, project management, and capture management in fostering sustainable growth for SME manufacturers. Digital transformation has emerged as a transformative force, offering SME manufacturers unprecedented opportunities to optimize processes, enhance efficiency, and expand their market reach. Through the adoption of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing, SMEs can streamline operations, improve product quality, and swiftly adapt to changing consumer preferences. Moreover, effective project management plays a crucial role in driving sustainable growth by ensuring timely delivery of products, cost optimization, and resource utilization. By implementing robust project management methodologies such as Agile or Lean, SME manufacturers can mitigate risks, enhance collaboration, and accelerate innovation, thereby gaining a competitive edge in the market. Furthermore, capture management, encompassing the identification, qualification, and pursuit of business opportunities, is instrumental in securing long-term growth for SME manufacturers. By strategically identifying target markets, understanding customer needs, and developing tailored solutions, SMEs can effectively capitalize on emerging opportunities and strengthen their market position. However, harnessing the synergies between digital transformation, project management, and capture management requires a comprehensive strategic approach. SME manufacturers must invest in cultivating a culture of innovation, fostering cross-functional collaboration, and nurturing talent equipped with the requisite digital and managerial skills. Driving sustainable growth in SME manufacturing necessitates a holistic approach that integrates digital transformation, project management, and capture management strategies. By embracing technological advancements, optimizing operational processes, and seizing market opportunities, SME manufacturers can embark on a trajectory of sustained success in the ever-evolving landscape of manufacturing.

Background: Nursing students, as future healthcare professionals, must understand how artificial intelligence can impact patient care and clinical decision making. Aim: To assess nursing students perspective about integration of artificial intelligence into health care services. Study design: Descriptive correlational, design was utilized. Setting: was conducted at Faculty of Nursing, Assiut university. Subject: 551, nursing students from the final academic year = 291 and 260 nursing student from internship year. Methods: Data were collected using : I- Personal data form, II- Nursing students awareness and knowledge regarding integration of AI into healthcare services, and III- Perception about integration of AI into healthcare services, IV- Nursing students attitude toward integration of AI into healthcare services. Results: the study revealed that nearly three fourths of study sample aware regarding to their level of total awareness toward AI (73.3%) , and two thirds of them are knowledgeable about AI, (66.6%) And more than half of the total studied nursing students have high level of total perception(51.4%), less than half of the total studied nursing students have moderate level of total perception(46.6. And (68.4%) of the total studied nursing students have positive attitude toward AI. Conclusion: there was a high statistical significant positive association among students knowledge, perception and attitude regarding AI integration in health care services at (P< 0.001). Recommendation: Encourage further researches into the long- termed impact of AI training on nursing practices, clinical decision-making, patient care, and healthcare outcomes. Key words: Integration of AI, awareness, knowledge, perception, attitude.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

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.

Purpose This paper delves into the transformative impact of artificial intelligence (AI) across diverse sectors, notably project management. It examines the potential of AI to revolutionize project management processes within entrepreneurial ventures, where agility, efficiency and innovation reign supreme. Design/methodology/approach Through a comprehensive analysis, this study navigates the intersection of AI and entrepreneurial project management. It meticulously dissects the opportunities AI presents, the hurdles it introduces and the optimal strategies for harnessing its capabilities effectively. Drawing insights from complexity theory, a framework is crafted to delineate AI’s capacity to substitute human involvement, elucidating key considerations for transitioning to a digitally-driven paradigm in entrepreneurial project management. Findings The study underscores AI’s potential to augment project management processes significantly, particularly in fostering agility and innovation. However, challenges persist, necessitating adept navigation to maximize AI’s benefits. The framework delineates the extent to which AI can supplant human roles, offering crucial insights into the digital transformation of entrepreneurial project management. Practical implications Practitioners are equipped with valuable guidance on leveraging AI effectively, enhancing organizational agility and performance. Understanding the implications of AI adoption fosters informed decision-making in the realm of project management. Social implications The integration of AI in entrepreneurial project management signifies broader societal shifts toward digitalization and automation. Insights from this study contribute to navigating these transformations, fostering greater resilience and adaptability in entrepreneurial endeavors. Originality/value This study offers a novel perspective on the intersection of AI and entrepreneurial project management, shedding light on unexplored terrain. By drawing on complexity theory, it advances a nuanced understanding of AI’s implications, paving the way for future research avenues in this dynamic field.

Background: Artificial intelligence (AI) is increasingly embedded in routine primary care, yet how the levels of integration might affect its acceptability is unknown, especially in relationship-based service models where patients expect visible human stewardship. Prior experimental studies often treat AI adoption as a binary condition, leaving the "intensity gradient" of automation and the role of model specialization under-explored. We examine whether increasing AI integration in the clinical encounter erodes patients' intention to seek care from family doctors in China, and whether labeling the AI as a medical-specific model buffers such erosion. Methods: We conducted a nationwide online survey experiment in China (N = 2790). Participants were randomly assigned to vignettes that varied by (i) the level of AI integration (low, medium, high) and (ii) the AI type (general-purpose vs. medical-specific large language model), with a human-only care scenario as a reference. Care-seeking intention from family doctors was assessed immediately after exposure. We estimated treatment effects using OLS regression with heteroskedasticity-robust standard errors, and examined the buffering hypothesis through an interaction term between AI integration intensity and AI type. Results: Care-seeking intention declined steadily as AI integration increased (p < 0.001), with the sharpest drop under high-intensity AI integration where clinical decisions were delegated to the AI system. Across all intensity levels, framing the system as a medical-specific AI consistently resulted in higher care-seeking intention than a general-purpose model. However, the interaction between AI intensity and the AI type was not statistically significant (p = 0.508). Conclusions: Patient acceptance of AI in primary care depends not only on whether AI is involved, but on how deeply AI is positioned in the encounter. Medical-specific AI labeling may enhance acceptance across all AI integration levels. The findings underscore the need to preserve human clinical agency in AI-embedded primary care. The results contribute to research on healthcare systems, digital health, and AI-patient interaction.

Cost overruns remain one of the most persistent challenges in construction and infrastructure project management, often undermining efficiency, sustainability, and stakeholder trust. With the rise of digital transformation, artificial intelligence (AI) and machine learning (ML) provide new opportunities to enhance predictive decision-making and strengthen project control. This study introduces a digital project management framework that integrates the Pelican Optimization Algorithm (POA) with Light Gradient Boosting Machine (LGBM) to deliver reliable and interpretable cost overrun forecasting. The proposed POA-LightGBM model leverages metaheuristic-driven hyperparameter optimization to improve predictive performance and generalization. A comprehensive evaluation using multiple error metrics Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) demonstrates that POA-LGBM significantly outperformed baseline LGBM and alternative metaheuristic configurations, achieving an average R2 of 0.9786. To support transparency in digital project environments, SHapley Additive exPlanations (SHAPs) were employed to identify dominant drivers of cost overruns, including actual project cost, energy consumption, schedule deviation, and material usage. By embedding AI-enabled predictive analytics into digital project management practices, this study contributes to advancing digital transformation in project delivery, offering actionable insights for cost control, risk management, and sustainable infrastructure development.

Artificial intelligence (AI) is transforming the education sector of different nations of the world. In spite of its commonly attested huge prospects, AI is yet to be integrated into Nigerian educational systems at a significant extent. This study examines the prospects and challenges of AI integration into Nigerian educational systems. The study adopts Venkatesh’s Unified Theory of Acceptance and Use of Technology, which explains how technologies get accepted and used in society. Questionnaire, and library and internet materials are used to source data. Survey design and mixed method with their allied analytical tools are employed. The results show pedagogic, technological, informative, administrative and socio-economic benefits of AI integration into Nigerian educational systems. Besides, it identifies the challenges constraining AI integration into the sector and proffers some solutions. The study concludes that the current low level of AI awareness and integration in Nigeria limits the ideal extent of AI impact on Nigerian educational systems. Stakeholders should collaborate significantly in making efforts to increase AI integration and find lasting solutions to the problems of AI integration into the educational systems.

Artificial Intelligence (AI) is rapidly inducing revolutionary transformations in entire aspects of life. Integrating AI into the educational landscape is heralding a new era of pedagogical innovations and redefining learning experiences. As AI technologies continuously evolve, the integration of AI-driven systems in the instructional settings is an inevitable progression and a formidable opportunity. This study is a scholarly attempt to explore the multi-faceted implications of AI integration in the spectrum of education highlighting its potential to revamp the traditional models. Thorough analysis of extant literature reveals that AI-driven platforms facilitate personalized learning experience which can accommodate diverse needs and learning paces of individual students. In addition to this, AI technologies have the potential to revolutionize assessment techniques. As the conventional assessment models fail to capture the true reflections of learning progress, AI assisted models can track, measure, analyze the academic output and provide real-time feedback which is transparent and free form bias. Moreover, AI-powered models have streamlined the administrative tasks as well. By automating routine jobs, optimizing resource allocation, and improving decision making, AI is minimizing human effort and maximizing productivity. However, AI integration poses some challenges and drawbacks. The concerns like data privacy and security, inequality, accessibility, algorithm bias, dehumanization, over-reliance on AI technologies and other ethical considerations are also looming. This study also presents certain future directions and policy perspectives on how to overcome these challenges and build an educational landscape which has the potential to fully utilize the AI-driven systems for academic achievement and learning progress of the students. As AI is revolutionizing entire spheres of life including education, it is imperative to critically engage with both opportunities and challenges posed by AI ensuring the principles of fairness, inclusivity, and integrity.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.