Course Description:

This 2-day bootcamp introduces engineering professionals to the practical integration of artificial intelligence (AI) into modern engineering workflows through a co-engineering approach that bridges emerging AI capabilities with traditional engineering expertise. Designed for a broad engineering audience, the course demonstrates how AI can enhance data analysis, support design processes, streamline workflows, and improve decision-making across project lifecycles. Participants will gain hands-on exposure to AI-enabled data review, data modeling, and workflow automation, while also being introduced to advanced simulation concepts and probabilistic approaches at a practical, high-level perspective. The course further emphasizes responsible AI adoption, including data governance and professional accountability, equipping participants with both the technical awareness and strategic insight needed to effectively and confidently integrate AI into their organizations.

Learning Objectives:

1. To develop a clear understanding of how AI technologies can be integrated into engineering workflows across a broad range of infrastructure, construction, and related disciplines.
2. To build proficiency in AI-powered data review and analysis, enabling engineers to extract actionable insights from complex datasets with greater speed and accuracy.
3. To develop competency in AI-assisted data generation and visualization techniques, equipping professionals to responsibly address data gaps in engineering analysis and design.
4. To enhance the ability to leverage AI platforms for workflow optimization, including automation of routine tasks, improved coordination, and development of data-driven decision-support tools.
5. To gain a practical, high-level understanding of simulation and probabilistic analysis approaches as tools for engineering decision support.
6. To enable effective co-engineering practices by integrating AI capabilities with engineering expertise.
7. To understand and apply responsible AI adoption practices in engineering organizations, including ethical considerations, data governance, and professional accountability frameworks.

Course Content:

• AI-Enabled Engineering Data Analysis for Infrastructure and Construction Applications.
• AI-Assisted Modeling and Synthetic Data Generation for Engineering Systems and Design Support.
• Introduction to Simulation and Probabilistic Analysis Techniques for Engineering Decision Support.
• Machine Learning Applications in Asset Performance Prediction, Risk Assessment, and Lifecycle Cost Optimization.
• Real-Time Operational Dashboard Development and AI-Driven Workflow Automation Across Engineering and Project Management Systems.