AI FOR ARCHITECTS

Get to know your instructor, the course roadmap, and what’s coming your way. We’ll cover all the questions, so you’re all set to dive in without surprises. This is your warm-up lap.

• Instructor introduction
• General housekeeping
• Course & assignments overview

Explore how AI is transforming the architect’s role, reshaping workflows, required skills, and the tools we rely on across the AEC industry. By examining emerging roles, core AI systems, and real-world examples, we’ll unpack where architects must adapt—and where AI will adapt to us.

• Evolving roles in architecture 
• High‑value vs. low‑value tasks
• AI systems & models relevant to AEC
• AI-driven workflow transformation
• Risks, limitations & strategic considerations
• Case Studies: Architecture specific AI apps and how they assist (Finch, x-figura, Aino)

Discover how to use AI as a creative partner to accelerate early-stage design thinking. You’ll learn how to translate abstract ideas into structured prompts, generate multiple concept directions, and iterate rapidly using LLMs. A hands-on workshop will guide you through developing a clear, presentation-ready concept direction.

• Translating abstract ideas
• What are LLMs?
• AI-assisted brainstorming
• Human-AI collaboration
• Prompt generation & Iterative design
• Workshop: Use an LLM to rapidly generate and compare multiple concept directions (text + structured criteria) ready to develop into client-facing narratives and visuals

Assignment #1: LLM-Powered Ideation Board
Select one direction and build a presentation-board plan, specifying the drawing types, key captions, and the prompts you’ll use to produce each output

Learn how AI is reshaping the way architects visualise ideas in the workplace. Explore how diffusion models and image‑generation tools can turn sketches, diagrams, and concepts into rich visual feedback that accelerates storytelling and design exploration.

• Diffusion and AI Image generation models
• Visualisation for design feedback 
• AI image generation 
• Ethics of AI-generated imagery
• Demo: Explore sketch/image to image workflows

Use advanced AI image workflows to generate, refine, and evolve architectural visuals with speed and precision. You’ll explore node-based systems, image-to-image transformation, and video generation techniques to test atmosphere, materiality, and spatial intent across multiple iterations. 

• Node based workflows
• Massing-to-image techniques 
• Image-to-video workflows
• Training custom images and styles
• Workshop: Explore sketch/image to image workflows

Assignment #2: Generative Visual Workflow
Create a workflow for one design using any of the generative visualising techniques explored in class.

Explore how generative 3D AI tools can rapidly transform early ideas into spatial forms ready for exploration and refinement. You’ll work across text, sketches, and reference inputs to generate 3D concepts, then adapt and integrate them into standard modelling environments for design development. 

• 3D generative AI
• Multimodal models
• Gaussian splatting
• Workflow integration strategies
• Workshop: Generate 3D forms using AI tools, refine them, and bring results into standard modelling environments

Learn how to turn BIM and graph‑based generation into your automated co‑designer — rapidly creating plans, filling entire buildings with layouts, and giving you instant performance feedback as you iterate. Let AI handle the heavy lifting of data integration and layout automation so you can focus on refining smarter, more informed designs.

• Graph-based plan generation
• BIM as computational system
• AI automation in BIM workflows
• Cross-disciplinary data integration
• Performance-driven feedback loops 
• Demo: Automatically fill stories and units with floor plans. Revise and refine your designs 

Assignment #3: Generative BIM Workflow
Create a workflow for one design using any of the generative visualising techniques explored in class.

Learn how AI agents, geospatial data, and automated analysis workflows can rapidly evaluate sites, uncover constraints/opportunities, and support early‑stage strategic planning.

• What are AI agents
• Site analysis requirements 
• Importance of data sources
• How to use AI to analyse datasets
• Demo: Putting an AI agent to use assessing site development feasibility

Assignment #4: Site Evaluation Study 
Pick a site and evaluate it for a project brief.

Discover how AI-powered simulation tools are enabling sustainability to be embedded from the very start of design. You’ll learn how surrogate models allow rapid testing of wind, solar, and thermal conditions, helping you make performance-driven decisions before detailed modelling begins. 

• Limitations of traditional simulation
• AI surrogate climate models
• Urban heat and infrared analysis
• Pedestrian wind comfort studies
• Performance-led design decisions
• Demo: Use AI simulation to make data-driven sustainable design decisions

Learn how to enhance parametric design workflows using AI co-pilots to extend, optimise, and automate design logic. You’ll experiment with generating custom behaviours and solving design problems without traditional coding barriers. 

• AI coding co-pilots overview
• Extending design software functionality
• Parametric model manipulation
• AI-driven optimisation methods
• Integrating logic into workflows
• Workshop: Optimise your designs using AI tools to help solve problems

Discover how AI applications are built from the ground up and how architects can prototype their own tools. You’ll explore APIs, model selection, and lightweight app structures to understand how design-focused AI tools are created and deployed. 

• Understanding APIs and AI requests
• How design AI apps are built
• Model selection and trade-offs
• Front-end and back-end basics
• Tool prototyping strategies
• Demo: Development of a custom AI tool prototype for architecture

Examine the ethical responsibilities of using AI in architectural practice, from dataset bias to authorship and accountability. You’ll critically evaluate how to adopt AI responsibly while maintaining design integrity and professional standards.

• Ethical implications of AI in design
• Dataset bias and distortion risks
• Authorship and IP considerations
• Professional accountability
• Responsible AI adoption
• Case Study: AI design controversies 

Consolidate everything learned throughout the course and present your final project idea. You’ll share your AI-driven design workflow, reflect on your process, and receive feedback in a group showcase setting. 

• Course wrap-up
• Final project showcase
• Q&A