Transformative Technologies in Construction and Project Management: 2025 Outlook and Survey Results
Industry perspectives and our plan for an annual survey.
Highlights
The construction and project management industry is rapidly evolving due to transformative technologies such as Gen-AI and Extended Reality.
A survey of twenty industry experts identified three categories of functions impacted by AI: efficiency functions, workflow functions, and human-centric functions.
Participants believe Gen-AI shows the highest impact in structured tasks such as scheduling, budgeting, and risk management, while Extended Reality enhances collaboration and training.
The industry anticipates continued AI and XR integration, automation in construction, and the rise of hybrid AI models in 2025.
The study aims to expand into an annual survey in collaborations with universities and industry leaders worldwide.
The world is undergoing a period of unprecedented transformation, and the construction and project management industry is no exception.
Rapid advancements in technology, coupled with evolving societal and environmental demands, are reshaping how projects are conceptualized, and delivered. Amidst this flux, understanding the role of transformative technologies in enhancing business processes is critical for construction and project management firms, and professionals alike.
We consulted with twenty industry practitioners and experts from our EPM Network, representing different regions across the globe. This report reflects the collective insights and expertise of professionals deeply engaged in the fields of construction and project management, who are using transformative technologies at the forefront of engineering projects.
The results revealed three distinct categories of construction and project management functions being affected. Interestingly, these categories of functions can be explained by their reliance on human activity into efficiency functions, workflow functions, and human-centric functions.
Our experts suggest a future of faster, more streamlined project delivery and development with AI; however, its impact varies across different functions.
This article explores key insights from our study, covering the following topics:
Objectives and Audience
Transformative Forces in 2024
Survey Design
Results
Impacts of Generative AI
Impacts of Extended Reality
What to Expect in 2025
Major Projects Trends in Canada
Discussion: Perception and Reality
Conclusion and This Year’s Plan
At the EPM Research Group, we aim to combine rigorous academic research with practical industry insights. We are part of the University of Calgary’s Civil Engineering Department and backed by the Engineering Project Management Endowment.
Objectives and Audience
This study was commissioned as a proof of concept to gauge interest, assess feasibility, and address validity concerns for an annual industry survey program on the impact of transformative technologies on construction and project management.
We asked experts about the impacts of transformative technologies in construction and project management and their opinions on questionnaire design.
Our vision is to create an annual survey to capture the pulse of the industry and its technological transformations year after year. We hope to co-create a resource that informs, inspires, and empowers stakeholders across the industry. The objectives of this survey program are:
Assessing the current state of industry adoption of transformative technologies.
Understanding technology adoption across various sub-sectors of the construction industry and across different countries and regions.
Exploring professional perspectives on the potential, limitations, and challenges of these technologies.
Examining the industry’s outlook on disruptive transformations.
Guiding academic research by highlighting industry’s emerging needs.
We defined the sample population as industry practitioners (both governmental agencies and private industry) in construction and project management from around the world who are at the forefront of leveraging transformative technologies in their projects.
The study benefits a diverse audience in construction and project management, from engineers and project managers to business leaders, academics, policymakers, and young engineers looking to navigate their careers.
This range of audiences and stakeholders reflects the interconnected nature of our industry.
Transformative Forces in 2024
We focused on two transformative technologies: Generative AI and Extended Reality.
These technologies were selected for their significant emergence in 2024 and their potential to reshape the construction and project management landscape.
Generative AI
Generative Artificial Intelligence (Gen-AI)—encompassing technologies such as Large Language Models (LLMs) and Large Image-Generative Models (LIGMs)—has emerged as a transformative force, redefining industries at an unprecedented pace.
The rapid ascent of Generative AI is affecting both academic research and technology development across many industrial sectors.
Gen-AI distinguishes itself by its ability to create new content—text, images, and code—that mirrors its training data. Unlike traditional AI, which primarily analyzes or classifies, Gen-AI extends beyond existing data by generating novel outputs.
The adoption of Gen-AI in construction and project management remains in its early stages, often limited to proof-of-concept applications [1]. Several challenges hinder widespread adoption, including validating AI-generated outputs, fine-tuning models for domain-specific applications, mitigating biases, ensuring data security, and addressing ethical implications.
In 2024, OpenAI introduced GPT-4o, a multimodal model capable of processing and generating text, images, and audio in real-time, setting new benchmarks in voice recognition and translation. The year also marked a dual surge in the rise of open-source models and the adoption of smaller, more efficient AI systems [2].
Open-source releases, such as Meta’s LLaMA 3 and Databricks’ DBRX, aimed to democratize AI and challenge the dominance of proprietary giants like OpenAI [3,4]. Alibaba joined this movement by releasing over 100 open-source models, including text-to-video technology, expanding AI’s applications in automotive, gaming, and scientific research [5].
In parallel, companies such as DeepSeek are reducing training costs through innovative methods, including the Mixture-of-Experts (MoE) approach, which routes tasks to specialized expert models for greater efficiency. This technique enables AI models to be trained at a fraction of the cost compared to traditional methods [6].
Simultaneously, the industry is embracing compact, task-specific models designed for efficiency and affordability, prioritizing practical deployment and accessibility across a range of applications [7].
Extended Reality
Extended Reality (XR)—encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—was another transforming force in 2024, aiming to change the way we work, learn, and connect by merging physical and digital spaces.
Despite hardware limitations, XR remains at the forefront of technological development due to its potential to revolutionize collaboration, training, and problem-solving. By enabling shared virtual spaces, XR allows teams to brainstorm, simulate, and interact with digital objects in real time, fostering seamless collaboration across virtual landscapes. XR provides hyper-realistic simulation environment for skill development and risk-free scenario testing [8].
In construction and project management, XR can transform training, remote operations, and stakeholder collaboration [9]. Immersive simulations allow workers to practice operating machinery, navigate complex sites, and respond to emergencies within risk-free, hyper-realistic environments [10,11]. Virtual offices enable stakeholders to explore digital twins of projects, refine designs, and collaboratively resolve physical and logistical challenges in real time [12].
The year 2024 marked a significant leap in XR advancements. Apple introduced the Vision Pro [13], a mixed-reality headset that seamlessly blends digital and physical worlds, offering professionals a powerful tool for reimagining project visualization and collaboration.
Not to be outdone, Meta unveiled the Orion glasses [14], lightweight AR eyewear that overlays virtual objects onto the real environment, enhancing on-site decision-making and stakeholder engagement. Google entered the arena with Android XR [15], an operating system tailored for XR devices, paving the way for a new wave of applications that could revolutionize how we approach construction projects.
Survey Design
The survey structure for this study consisted of three main components: Gen-AI Impact, Extended Reality Impact, and Industry Outlook.
Before answering the questions, participants were provided with descriptions of the strengths and weaknesses of Gen-AI and Extended Reality based on a systematic literature review we conducted.
1- Generative AI Impact
Participants evaluated the potential for productivity gains that Generative AI could bring to 14 functions and processes within construction and project management.
Each area was rated on a scale from 1 to 9, with 9 indicating the highest potential for productivity gains. The 14 functions and processes within construction and project management included are as follows:
Project Planning and Scheduling: Streamlining timelines, resource allocation, and schedule resilience.
Budgeting and Cost Estimation: Automating cost forecasts and refining financial accuracy.
Risk Management: Enhancing risk identification and mitigation strategies using AI-driven insights.
Project Controls: Improving monitoring, execution, and corrective actions.
Procurement and Supply Chains: Optimizing vendor selection and contract management processes.
Contract Management and Tendering: Streamlining bid evaluations and contract negotiations.
Layout Planning and Logistics: Advancing site layout designs and resource movement efficiency.
Quality Control and Compliance: Automating quality assurance processes and ensuring regulatory adherence.
Stakeholder Engagement: Personalizing communication strategies and improving stakeholder alignment.
Lessons Learned and Continuous Improvement: Enabling systematic project close out reports, reviews, and knowledge-sharing.
Human Resources and Staffing: Enhancing workforce planning and skills development.
Occupational Health and Safety: Leveraging AI to monitor and improve safety measures.
Trend and Change Management: Managing project change orders and their impact dynamically.
Conflict Resolution: Facilitating proactive identification and resolution of disputes.
These fourteen construction and project management functions were described in greater detail in the survey compared to this report. The descriptions helped to establish a common baseline of understanding.
2- Extended Reality Impact
The survey included targeted questions about the familiarity of participants with Extended Reality (XR) technologies. This set of questions aimed to understand the current level of adoption and firsthand experience with XR technologies in the industry.
Next, participants were asked which of the previous functions in construction and project management would most benefit from the convergence of Generative AI and XR. The question further encouraged respondents to reflect on the practical applications of combining AI-driven insights with immersive XR tools.
3- Industry Outlook
Lastly, the participants were asked to reflect on their outlook for the coming year. This question was further discussed with some of participants over conference calls to grasp their point of view on the technological trends.
Results
In total, twenty industry experts participated, offering perspectives on the areas where Generative AI might exert the greatest influence. The respondents were asked to rank fifteen critical domains in construction and project management on a scale from 1 to 9, with 9 indicating the highest potential and 1 reflecting minimal impact.
Impacts of Generative AI
The results, visualized in the spider plot of Figure 1, provide a clear snapshot of how industry experts perceive the varying impacts of Generative AI in productivity gains across different construction and project management functions.
We refrained from employing more time-intensive pairwise comparisons in favor of simple Likert scales to keep the survey efficient for participants. Despite this simplification, the results revealed distinct patterns in the data, showing clear variations in the perceived impact of generative AI across functions.
Clustering of Generative AI Impact Areas
We identified three distinct groupings based on respondents' perceptions of the relative potential for productivity gains.
These groupings allowed us to classify construction and project management functions into three categories:
Efficiency Functions, which saw the highest perceived impact (e.g., planning, scheduling, and cost estimation);
Workflow Functions, reflecting moderate potential (e.g., logistics, procurement, and compliance); and,
Human-Centric Functions, where the impact was perceived to be lower due to the reliance on interpersonal skills and decision-making (e.g., stakeholder engagement and conflict resolution).
Figure 2 depicts this categorization and the areas where generative AI can complement and enhance current practices.
Efficiency Functions: Generative AI demonstrated the highest potential in domains relying on structured workflows, repetitive updating, and large datasets. Participants saw immediate value in leveraging AI to increase productivity on these activities.
Project Planning and Scheduling: Automating schedule templates, identifying potential bottlenecks, and analyzing historical data.
Budgeting and Cost Estimation: Accelerating conceptual cost estimating, cost breakdown generation, and anomaly detection.
Risk Management and Project Controls: Generating predictive insights, improving risk analysis, and creating automated dashboards.
Contract Management and Tendering: Automating contract drafting, vendor analysis, and tender evaluations.
Lessons Learned: Compiling and analyzing project histories to identify best practices and inform future strategies.
Workflow Functions: The synergy between AI tools and human judgment was emphasized due to the often time intensive workflow and case specific context of these functions. These domains benefited from AI's ability to handle routine, data-intensive tasks while relying on humans to navigate the process:
Procurement and Supply Chains: Automating RFPs and supplier evaluations while preserving human oversight for negotiations.
Layout Planning and Logistics: Proposing site layouts and logistics scenarios, adaptable by human planners.
Quality Control and Compliance: Standardizing checklists and identifying gaps, with human-led physical inspections.
Human Resources and Staffing: Streamlining applicant processing while retaining human interactions for cultural fit assessments.
Project Trends and Change Management: Identifying trends and suggesting changes while requiring expert integration.
Occupational Health and Safety: Drafting safety policies with humans ensuring environment-specific adaptation.
Human-Centric Functions: These domains—heavily influenced by interpersonal skills and real-time decision-making—showed moderate or lower potential for AI adoption. While AI can assist with tools like sentiment analysis and automated reports, the human element remains crucial:
Stakeholder Engagement: Supporting sentiment summaries but maintaining trust-building as a human-led activity.
Conflict Resolution: Analyzing conflict data to suggest strategies, yet relying on empathy and nuanced judgment for resolutions.
While this preliminary survey respondent pool was limited to only twenty experts, it highlighted an interesting delineation between tasks where AI can lead, collaborate, or support.
Impacts of Extended Reality
After exploring the perceived impact of Generative AI, we assessed participants’ familiarity with Virtual Reality (VR) and Augmented Reality (AR) technologies.
About 40% of the participants reported having used these tools, highlighting relatively limited adoption of XR in construction and project management.
Despite limited exposure to XR technologies, respondents demonstrated significant optimism about the potential for combining XR with Generative AI to address critical industry challenges. This convergence offers unique opportunities in areas such as project controls, 4D/5D scheduling, risk management, layout planning, and procurement. Figure 3 highlights the perception of participants as to which function stands to gain the most from integration of XR and Generative AI.
A common theme in our conversations was around how by harnessing the immersive capabilities of XR, professionals can simulate AI-generated scenarios, enabling them to visually explore and interact with project data in real-time.
What to Expect in 2025
Lastly, participants were asked to share their outlook for the coming year, with follow-up discussions conducted through one-on-one calls to better understand their perspectives on technological trends.
At the Engineering Project Management (EPM) research group, our research is focused on three of these transformative themes shaping the future of construction and project management, marked below with an asterisk: the integration of AI and XR technologies, Hybrid ML-LLM AI models, and the adoption of Small Language Models (SLMs) with edge computing.
1- AI and XR Integration*
The integration of AI and XR has emerged as a key trend, identified by survey respondents and widely recognized across the industry.
Meta has addressed the demand for user-friendly devices with the Ray-Ban Meta Smart Glasses, a lightweight option featuring a camera and Generative AI capabilities, though it lacks full XR functionality [16]. This trade-off prioritizes wearability and everyday use. Looking ahead, Meta is developing Orion AR glasses, which promise advanced AR and AI integration in a sleek form [14]. Expected by late 2027, these glasses reflect Meta's focus on overcoming current limitations in wearable technology [4].
2- AI in Automation and Robotics
By 2025, AI-driven automation and robotics are set to transform the construction industry, streamlining processes and enhancing safety. Advanced robotics will take on complex tasks with unmatched precision and speed, reducing physical strain on workers and mitigating risks in hazardous environments [17].
Autonomous drones equipped with AI will revolutionize site management by conducting inspections, monitoring progress, and identifying potential issues like structural weaknesses or material shortages in real time. These insights will enable project managers to optimize resources, maintain schedules, and make data-driven decisions [18].
Tesla is advancing AI robotics with innovations like 'Optimus,' which could be adapted for construction tasks. Such robots, capable of performing diverse manual activities, have the potential to reduce labor-intensive work and further expand the scope of automated construction technologies [19].
3- Hybrid ML-LLM AI Models*
LLMs excel in capturing linguistic patterns, generating coherent text, and generalizing across tasks. Their ability to perform hierarchical task planning through analogical reasoning has become central to LLM research. However, traditional AI and machine learning models surpass LLMs in tasks requiring accuracy and prediction. By integrating LLMs' task decomposition with ML data-driven forecasting, a hybrid ML-LLM AI approach offers transformative solutions in construction and project management.
ML can analyze historical project data to predict risks, optimize processes, and identify patterns that lead to inefficiencies, enabling proactive management [20]. LLMs communicate these results with diverse teams, automate documentation, and generate compliance reports and proposals, while reducing administrative effort [21,22].
4- Open Source / Small Language Models (SLM) and Edge Computing*
Open Source and Small Language Models (SLMs) represent a transformative opportunity for construction and project management, offering cost-effective, targeted solutions to manage complex data and communication flows.
Models like GPT-4o-mini and Gemini-flash provide a streamlined alternative to Large Language Models (LLMs), enabling organizations to deploy powerful natural language processing applications with significantly reduced investment [7].
Beyond cost efficiency, the value of SLMs lies in their adaptability; they can be refined and deployed on local servers disconnected from the broader internet which is an immense advantage when dealing with proprietary information and sensitive project data.
SLMs are also well-suited for integration with XR (Extended Reality) technologies, which often operate on portable devices with limited processing power. This synergy facilitates real-time, context-sensitive support through augmented reality overlays and virtual environments, enhancing operational efficiency as well as improving training and safety protocols [23].
5- Generative AI Design and Architecture
A few experts in our survey population were eagerly monitoring the impact of Generative AI on architectural design in 2025.
The Large Image Generation Models (LIGM) are set to significantly expanding the way architects approach the creation and refinement of building concepts. This cutting-edge technology enables the input of design parameters and constraints into AI systems, which then generate a wide array of innovative design options [24].
6- Avatars Everywhere
The concept of avatars—digital personas that represent users in virtual environments—has evolved significantly, becoming a versatile tool in various industries. In 2025, these digital representations are not only personal avatars in social media or gaming but have expanded into professional fields, offering a unique way to interact within digital spaces. Avatars can be customized to mirror the user’s physical appearance or embody a completely designed persona, equipped with realistic expressions and movements [25].
Major Projects Trends in Canada
Understanding major transformative trends in construction and project management benefits from a parallel awareness of the broader landscape of construction project prevalence.
Figure 4 and 5 show the annual trend in project numbers and capital costs for both the resource project and infrastructure projects in Canada as disclosed by Government of Canada open data initiative [26,27].
In terms of the number of projects in Canada, both resource and infrastructure projects have been on the rise in 2024. However, while capital expenditure for resource projects has shown a steady increase, infrastructure investment has declined from 2023 to 2024.
As shown in Figure 4, the energy sector, which remains a cornerstone, has seen consistent activity in electricity generation, oil and gas, and other projects such as biomass, biofuel, and geothermal production, with 340 projects contributing CA$510 billion by 2024. Mining, has exhibited robust growth, with investments growing from CA$89 billion to CA$117.1 billion in four years, driven by metals and an expanding focus on non-metals and critical resources.
Canada's infrastructure investment trends highlight a strategic shift between volume and value. In 2021, the number of projects (excluding those related to COVID 19 response) peaked at 1,377, supported by a record CA$20.94 billion in eligible costs, reflecting a major post-pandemic recovery push. While project counts dropped in subsequent years—reaching 643 in 2022 and 486 in 2023—investment levels remained significant. By 2024, project counts rose again to 802, supported by CA$5.77 billion in funding.
Discussion: Perception and Reality
The rapid evolution of AI continues to reshape industries.
The recent emergence of powerful open-source models by DeepSeek in early 2025 challenged the consensus on proprietary models, highlighting the volatility of the Gen-AI scene and the infancy of this technological race [28].
AI's trajectory remains one of continuous advancement, with no definitive winner yet, as new models and capabilities emerge at an accelerating pace.
As suggested by a curious paradox known as Jevons Paradox, technological efficiency does not always lead to reduced demand [29]. On the contrary, as seen with the steam engine and many other era-defining inventions, improvements in efficiency often result in increased overall demand and consumption.
In construction and project management, the introduction of Gen-AI does not appear to be replacing workers yet. However it has helped in eliminating bottlenecks and increasing the quality and speed of project delivery.
The limited consultations we had with industry experts through this study suggest that instead of seeing lower employment, firms are facing a greater need for skilled professionals. The overall rise in the number of major projects announced in Canada in 2024 may indicate a trend that aligns with efficiency gains.
The reliability of Generative AI models remains a key consideration. Many of the most sensitive functions in construction and project management carry significant liabilities and require human oversight. Gen-AI models can drastically reduce the cost of first drafts, but final work products require accountability that must remain with experienced professionals.
For XR technologies, hardware limitations, including integration challenges and high costs, remain key bottlenecks. Addressing these issues is essential for realizing XR's full potential and achieving seamless adoption across business processes.
Looking ahead, the challenge is not whether AI will reshape the industry—it already is—but how organizations will adapt to these changes.
Conclusion and Next Year’s Plan
Looking ahead to 2025, transformative advancements in AI and XR are expected to drive significant changes in the construction and project management industry, reshaping workflows and enhancing the integration of digital and physical environments.
Our goal for 2025 is to expand the scope of this study, both in reach and depth, into an industrywide survey of the construction and project management industry. We have established contacts with several universities around the world to partner on this survey.
We aim to engage a wider audience across diverse geographies and disciplines to ensure the findings accurately reflect the industry's dynamic and interconnected nature.
Building on the insights gained from this study, we are refining our approach and designing a more precise and expressive questionnaire to capture nuanced perspectives on emerging trends within the construction and project management sector.
If you are interested in supporting this effort and sharing your insights on this year’s or next year’s survey, we would greatly value your contribution. Your perspectives are important for informing the design of next year’s survey. We invite you to connect with us.
Notes
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Refer to this article using the following citation format:
Zangeneh, P. and Ghorab, Kh. (2025), “Transformative Technologies in Construction and Project Management: 2025 Outlook and Survey Results”, EPM Research Letters.
References
Taiwo, R., et al. (2024). Generative AI in the construction industry: A state-of-the-art analysis. arXiv. https://doi.org/10.48550/arXiv.2402.09939
Ueno, L., & Lynn, T. (n.d.). GPT-4o: The comprehensive guide and explanation. Retrieved from https://blog.roboflow.com/gpt-4o-vision-use-cases/
Grattafiori, A., et al. (2024, November 23). The Llama 3 herd of models. arXiv. https://doi.org/10.48550/arXiv.2407.21783
Databricks. (n.d.). Introducing DBRX: A new state-of-the-art open LLM.
Wang, P., et al. (2024, October 3). Qwen2-VL: Enhancing vision-language model’s perception of the world at any resolution. arXiv. https://doi.org/10.48550/arXiv.2409.12191
Dai, D., et al. (2024, January 11). DeepSeek MoE: Towards ultimate expert specialization in mixture-of-experts language models. arXiv. https://doi.org/10.48550/arXiv.2401.06066
Nguyen, C. V., et al. (2024, October 25). A survey of small language models. arXiv. https://doi.org/10.48550/arXiv.2410.20011
Adami, P., et al. (2023). Participants matter: Effectiveness of VR-based training on the knowledge, trust in the robot, and self-efficacy of construction workers and university students. Advanced Engineering Informatics, 55, 101837. https://doi.org/10.1016/j.aei.2022.101837
Xu, Z., Feng, Z., Babaeian Jelodar, M., & Guo, B. H. W. (2024). Augmented reality applications in construction productivity: A systematic literature review. Advanced Engineering Informatics, 62, 102798. https://doi.org/10.1016/j.aei.2024.102798
Pooladvand, S., Taghaddos, H., Eslami, A., Nekouvaght Tak, A., & Hermann, U. (2021). Evaluating mobile crane lift operations using an interactive virtual reality system. Journal of Construction Engineering and Management, 147(11), 04021154. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002177
Chen, T., Yabuki, N., & Fukuda, T. (2024). Mixed reality-based active hazard prevention system for heavy machinery operators. Automation in Construction, 159, 105287. https://doi.org/10.1016/j.autcon.2024.105287
Sai, S., Sharma, P., Gaur, A., & Chamola, V. (2024). Pivotal role of digital twins in the metaverse: A review. Digital Communications and Networks. Retrieved February 2, 2025, from https://www.sciencedirect.com/science/article/pii/S2352864824001706
Apple. (n.d.). Apple Vision Pro. Retrieved January 5, 2025, from https://www.apple.com/apple-vision-pro/
Meta. (n.d.). Introducing Orion, our first true augmented reality glasses. Retrieved January 5, 2025, from https://about.fb.com/news/2024/09/introducing-orion-our-first-true-augmented-reality-glasses/
Android. (n.d.). Learn more about Android XR. Retrieved February 2, 2025, from https://www.android.com/xr/
Meta. (n.d.). Introducing the new Ray-Ban | Meta smart glasses. Retrieved January 5, 2025, from https://about.fb.com/news/2023/09/new-ray-ban-meta-smart-glasses/
Wanna, S., Parra, F., Valner, R., Kruusamäe, K., & Pryor, M. (2024). Unlocking underrepresented use-cases for large language model-driven human-robot task planning. Advanced Robotics, 38(18), 1335–1348. https://doi.org/10.1080/01691864.2024.2366974
Waqar, A., et al. (2023). Modeling relation among implementing AI-based drones and sustainable construction project success. Frontiers in Built Environment, 9, 1208807. https://doi.org/10.3389/fbuil.2023.1208807
Malik, A. A., Masood, T., & Brem, A. (2023, April 11). Intelligent humanoids in manufacturing to address worker shortage and skill gaps: Case of Tesla Optimus. arXiv. https://doi.org/10.48550/arXiv.2304.04949
Erfani, A., & Cui, Q. (2022). Predictive risk modeling for major transportation projects using historical data. Automation in Construction, 139, 104301. https://doi.org/10.1016/j.autcon.2022.104301
Ghimire, P., Kim, K., & Acharya, M. (2024). Opportunities and challenges of generative AI in the construction industry: Focusing on adoption of text-based models. Buildings, 14(1), 220. https://doi.org/10.3390/buildings14010220
Ghimire, P., Kim, K., & Acharya, M. (2023). Generative AI in the construction industry: Opportunities & challenges. arXiv. https://doi.org/10.48550/arXiv.2310.04427
Xu, F., Zhou, T., Nguyen, T., Bao, H., Lin, C., & Du, J. (2025). Integrating augmented reality and LLM for enhanced cognitive support in critical audio communications. International Journal of Human-Computer Studies, 194, 103402. https://doi.org/10.1016/j.ijhcs.2024.103402
Li, C., Zhang, T., Du, X., Zhang, Y., & Xie, H. (2024). Generative AI models for different steps in architectural design: A literature review. Frontiers of Architectural Research. Retrieved February 3, 2025, from https://www.sciencedirect.com/science/article/pii/S209526352400147X
Ante, L., Fiedler, I., & Steinmetz, F. (2023). Avatars: Shaping digital identity in the metaverse.
Natural Resources Canada. (n.d.). Natural resources: Major projects planned or under construction 2024 to 2034. Retrieved February 3, 2025, from https://natural-resources.canada.ca/science-and-data/data-and-analysis/natural-resources-major-projects-planned-or-under-construction-2024-2034/26583
Infrastructure Canada. (n.d.). Housing, infrastructure and communities Canada - Housing and infrastructure project map. Retrieved February 3, 2025, from https://housing-infrastructure.canada.ca/gmap-gcarte/index-eng.html
DeepSeek-AI. (2025, January 22). DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning. arXiv. https://doi.org/10.48550/arXiv.2501.12948
York, R., & McGee, J. A. (2015). Understanding the Jevons paradox. Environmental Sociology, 2(1), 77–87. https://doi.org/10.1080/23251042.2015.1106060
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