Digital, Volume 5, Issue 3 (September 2025) – 4 articles

An Application Programming Interface (API) is a formally defined interface that enables controlled interaction between software components, and is a key pillar of modern microservice-based architectures. However, asynchronous API changes often lead to breaking compatibility and introduce systemic instability across dependent services. Prior research has explored various strategies to manage such evolution, including contract-based testing, semantic versioning, and continuous deployment safeguards. Nevertheless, a comprehensive orchestration mechanism that formalizes dependency propagation and automates compatibility enforcement remains lacking. In this study, we propose a Compatibility-Driven Version Orchestrator, integrating semantic versioning, contract testing, and CI triggers into a unified framework. We empirically validate the approach on a Kubernetes-based environment, demonstrating the improved resilience of microservice systems to breaking changes. This contribution advances the theoretical modeling of cascading failures in microservices, while providing developers and DevOps teams with a practical toolset to improve service stability in dynamic, distributed environments. Full article

The integration of Artificial Intelligence (AI) in construction project management is revolutionising the industry; offering innovative solutions to enhance efficiency, reduce costs, and improve decision making. This structured literature review explored the current applications, benefits, challenges, and future trends of AI in construction project management. This study synthesised findings from 135 peer-reviewed articles published between 1985 and 2024; representing Industry 3.0 (3IR), Industry 4.0 (4IR), and Industry 4.0 Post COVID-19 (4IR PC). Analysis showed that the Planning and Monitoring and Control phases of the project have the greatest application of AI, while decision making, prediction, optimisation, and performance improvement are the most common purposes of AI use in the construction industry. The drivers of AI adoption within the construction industry include technology availability, project outcome and performance improvement, a competitive advantage, and a focus on sustainability. Despite these advancements, the review revealed several barriers to AI adoption, including data integration issues, the high cost of AI implementation, resistance to change among stakeholders, and ethical concerns surrounding data privacy, amongst others. This review also identified future ongoing applications of AI in the construction industry, such as sustainability and energy efficiency, digital twins, advanced robotics and autonomous construction, and optimisation. By providing a comprehensive analysis of the evolution of practices and the future direction of AI application, this study serves as a resource for researchers, practitioners, and policymakers seeking to understand the evolving landscape of AI in construction project management. Full article

ChatGPT, a prominent large language model developed by OpenAI, has rapidly become embedded in digital infrastructures across various sectors. This narrative review examines its evolving role and societal implications in three key domains: healthcare, education, and the economy. Drawing on recent literature and examples, the review explores ChatGPT’s applications, limitations, and ethical challenges in each context. In healthcare, the model is used to support patient communication and mental health services, while raising concerns about misinformation and privacy. In education, it offers new forms of personalized learning and feedback, but also complicates assessment and equity. In the economy, ChatGPT augments business operations and knowledge work, yet introduces risks related to job displacement, data governance, and automation bias. The review synthesizes these developments to highlight how ChatGPT is driving digital transformation while generating new demands for oversight, regulation, and critical inquiry. It concludes by outlining priorities for future research and policy, emphasizing the need for interdisciplinary collaboration, transparency, and inclusive access as generative AI continues to evolve. Full article