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Program Curriculum

Six modules that take you from AI fundamentals to a costed adoption plan for your own firm — built on the Smoother methodology of theory, hands-on practice, and role simulation.

Every module pairs concepts with real built-environment practice and adapts to your role and discipline. Here's what each one covers.

How It Works

Each module blends a short concept segment with a hands-on activity, then a reflection tied to your own projects. Evaluations are applied per module and once program-wide, and are personalized to your role — executives get strategy-focused work; practitioners get hands-on scenarios.

1

AI Fundamentals for the Built Environment

What AI is, what it isn't, and why it matters for design and construction.

Learning objectives

  • Distinguish machine learning, computer vision, and generative AI
  • Identify where AI fits across the project lifecycle
  • Recognize AI's limits and common failure modes

Topics covered

  • Types of AI in engineering, architecture & construction
  • The AI toolkit available today
  • Data as the foundation for AI
  • Realistic expectations versus hype
Primary activity: Quiz + discussion
2

AI Tools for Design and Engineering

Hands-on with generative design, visualization, and analysis tools.

Learning objectives

  • Run a generative-design exploration end to end
  • Produce AI visualizations from a design brief
  • Use AI to analyze a document or dataset

Topics covered

  • Generative and parametric design
  • AI rendering and visualization
  • Structural and energy analysis aids
  • Integrating AI with BIM and CAD
Primary activity: Hands-on lab
3

AI in Construction Management

Scheduling, cost, safety, and quality on real projects.

Learning objectives

  • Apply AI to a scheduling or estimating task
  • Evaluate a site-monitoring approach
  • Plan an AI-assisted reporting workflow

Topics covered

  • Predictive scheduling and cost estimation
  • Computer-vision safety monitoring
  • Progress tracking and digital twins
  • Predictive maintenance of equipment and assets
Primary activity: Case study
4

Prompt Engineering for Professionals

Get reliable, useful output from AI assistants.

Learning objectives

  • Apply the CRAFT framework to real tasks
  • Build a reusable team prompt library
  • Recognize and correct AI hallucinations

Topics covered

  • The CRAFT framework
  • Role and context setting
  • Iteration and verification
  • Building shared prompt libraries
Primary activity: Practical exercise
5

Ethics, Compliance, and Responsible AI

Liability, privacy, bias, and professional responsibility.

Learning objectives

  • Identify liability and compliance risks
  • Establish a human-review checkpoint
  • Assess data-privacy implications of AI tools

Topics covered

  • Professional responsibility and liability
  • Data privacy and client confidentiality
  • Bias and fairness in AI systems
  • Governance and review processes
Primary activity: Role simulation
6

Building Your AI Strategy

Turn learning into an adoption plan for your firm.

Learning objectives

  • Assess your firm's AI readiness
  • Prioritize high-impact use cases
  • Draft a phased adoption roadmap

Topics covered

  • Readiness assessment
  • Use-case prioritization
  • Pilot design and success metrics
  • Change management and training
Primary activity: Capstone project

Capstone Project

You'll produce a phased AI adoption roadmap for your own organization — integrating your readiness assessment and a prioritized use-case shortlist — and present it for structured feedback. You leave with a plan you can act on, not just notes.

Ready to enroll?

Tell us your team, timeline, and goals, and we'll recommend the right format — from a one-hour express session to a 24-hour masterful track.

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