Welcome

This brief 5-minute survey aims to explore how Generative AI technologies, including Large Language Models (LLMs) and Large Image-Generative Models (LIGMs), can boost productivity in existing processes and practices across different sectors of Construction and Poject management.


We would appreciate your help with two requests:
1. Please take a moment (about 5 minutes) to complete the survey below.
2. If possible, kindly share this survey with three other professionals whose opinions on the subject you'd value.


In return,
We will provide you with an advanced copy of the results, our report on "Transformative Technologies in Construction and Project Management," and our peer-reviewed publication.

Your expertise is invaluable in identifying opportunities for innovation and efficiency improvements, guiding our research programs towards areas of greater value.
We are,
The Engineering Project Management (EPM) research group at the Schulich School of Engineering, University of Calgary. This survey is being conducted under the guidance of Dr. Pouya Zangeneh, the current chair of EPM. EPM mandate is to conduct core research that elevates the industry.

If you are not familiar with Generative AI, please read through the following sections, otherwise, please proceed to the next page
What is Generative AI:

Generative AI encompasses technologies designed to produce new content, such as text, images, and code, by learning from existing data. Unlike traditional AI, which interprets or classifies data, generative AI creates entirely new data that resembles its training material. This technology enables a variety of applications, from generating realistic visuals to writing coherent text, by understanding and replicating the patterns in its training datasets.

Large Language Models (LLMs) like OpenAI's GPT and Large Image-Generative Models (LIGMs) such as OpenAI’s DALL-E are key examples of generative AI. LLMs can automate the creation of project reports, correspondence, and planning documents, streamlining administrative tasks. LIGMs, meanwhile, offers the ability to generate detailed visualizations from project descriptions, enhancing planning and client presentations. Together, these technologies are poised to revolutionize efficiency, creativity, and decision-making in construction and project management by offering tools that automate complex tasks and foster innovative solutions.

Generative AI Shortcomings:

Despite the evident powers of Generative AI, the vast majority of domain specific applications of it are in proof-of-concept level. There are various reasons for lack of its adoption, but the following few are most cited issues:

1. Lack of Validation and Generalizability: Validating AI-generated content for accuracy and ensuring its applicability across different scenarios are significant challenges, limiting its reliability and broader use. Generative AI's struggle with understanding context or common-sense reasoning can result in inappropriate or irrelevant outputs.

2. Fine-Tuning and Control: Generative AI, while innovative in creating diverse outputs from images to text, faces a challenge in fine-tuning specific elements without altering the entire composition. This limitation affects models from LIGMs like Stable Diffusion, unable to edit parts of an image in isolation, to LLMs that struggle with nuanced text modifications.

3. Bias and Ethical Concerns: Generative AI can amplify biases from training data, leading to outputs that may perpetuate stereotypes or be ethically harmful. As mentioned before, AI Generative AI's struggle with understanding context.

4. Data Privacy and Security: The necessity to protect sensitive data and ensure compliance with regulations highlights the challenges in maintaining privacy and security.

5. Human-in-the-Loop: Balancing AI-driven automation with necessary human oversight to guarantee that AI recommendations are practical, safe, and in line with project objectives demands a delicate equilibrium between technology and human judgment.