Artificial Intelligence in Engineering Project Management

Course Description:

This course examines how artificial intelligence technologies can be integrated into engineering project management practices to enhance planning, forecasting, monitoring, and decision-making. The course begins by establishing the foundational principles of engineering project management, including the project lifecycle, scheduling techniques, cost estimation methods, and risk management frameworks. Understanding these traditional management systems is essential for evaluating how AI tools can complement and enhance existing project management practices.

Learning Objectives:

After completing this course, the participant should be able to:

1. Describe the foundational principles of engineering project management, including the project lifecycle, Work Breakdown Structures, scheduling methods, cost estimation practices, and project monitoring techniques.
2. Explain the role of artificial intelligence technologies—such as machine learning, predictive analytics, natural language processing, optimization algorithms, and computer vision—in modern engineering project management.
3. Identify the types of project data used by AI systems to analyze project performance, including scheduling records, procurement data, cost reports, safety documentation, and equipment performance information.
4. Evaluate how artificial intelligence can improve cost forecasting and schedule prediction by analyzing historical project performance data and identifying probabilistic risk patterns.
5. Analyze how AI-assisted risk management systems detect emerging technical, financial, safety, and regulatory risks during engineering project execution.
6. Explain how artificial intelligence supports workforce allocation, equipment utilization planning, procurement logistics, and multi-project resource coordination.
7. Describe how AI-enabled monitoring technologies—including digital project control systems, digital twins, drone-based monitoring, and computer vision—enhance project oversight and real-time performance analysis.
8. Assess the ethical responsibilities, professional oversight requirements, and regulatory considerations associated with the use of artificial intelligence in engineering project management.
9. Evaluate practical applications of AI technologies through engineering case studies involving infrastructure construction, industrial facility expansion, and construction safety monitoring.
10. Identify implementation strategies that engineering organizations can use to integrate artificial intelligence technologies into project management systems while maintaining professional accountability and regulatory compliance.