- Level: Intermediate
- Prerequisites: None
- Assessments: Weekly Micro Assessments; Full Assessment in the Final Week
Week 1: Introduction to AI in Engineering
Learning Outcome: Understand how AI is transforming engineering practices.1.1 Overview of AI applications in engineering.
1.2 Role of AI in design and optimization.
1.3 Case studies of AI in engineering successes.
Practical Component
Analyze engineering datasets to identify optimization opportunities.
Week 2: AI for Design Automation
Learning Outcome: Learn how AI automates engineering design.1.4 Parametric and generative design with AI.
1.5 Tools and software for AI-driven design.
1.6 Real-world examples of automated design solutions.
Practical Component
Use AI tools for a simple design task (e.g., circuit design).
Week 3: Predictive Maintenance and Failure Analysis
Learning Outcome: Use AI for predictive maintenance.1.7 AI-based fault detection in machinery.
1.8 Monitoring equipment performance with AI.
1.9 Predicting failures and downtime.
Practical Component
Develop an AI model for fault prediction.
Week 4: AI in Civil and Structural Engineering
Learning Outcome: Explore AI applications in construction and structural analysis.1.10 AI for construction project management.
1.11 Structural integrity analysis using AI.
1.12 Smart city planning with AI.
Practical Component
Simulate a structural analysis using AI tools.
Week 5: Ethical Considerations in Engineering AI
Learning Outcome: Implement AI responsibly in engineering.1.13 Data integrity and security concerns.
1.14 Avoiding biases in engineering AI models.
1.15 Environmental considerations in AI applications.
Practical Component
Evaluate an AI tool for ethical compliance.
Week 6: Final Project
Learning Outcome: Build an AI-powered engineering solution.1.16 Define a specific engineering challenge (e.g., bridge design).
1.17 Develop and test the solution.
1.18 Present the project to peers.
Practical Component
Deliver a functional AI engineering model.