What does it mean for AI to be a design material?

Designers and thought leaders on the cutting edge of technology have been calling artificial intelligence (AI) and machine learning (ML) a “design material” for a few years now, and so calling for designers to learn about AI in the way a potter might learn about clay, or a painter about oils.

When a potter learns about clay or a painter about oils, they have hundreds of years of craft practice to learn from. They can tangibly explore the clay or the oils with all their senses, and practice with them in the ways potters and painters have practiced for centuries.

How can one do that with AI? What does it mean to tangibly explore ephemeral software served up from a distant cloud? Who is one to learn from, given the foundational papers of the current generation of AI/ML are less than a decade old, and written by computer scientists, not designers?

A practical workshop teaching you material exploration of technology

Material exploration is a craft technique of hands-on experimentation with a material — fabric, wood, clay, metal — in order to figure out how to work with it. It’s material-specific, and the individual techniques used are as old as those crafts are.

In the late 2000s, designers and academics began formalizing ways of conducting material explorations of immaterial things, like technologies. They developed methods and practices for non-engineers to examine invisible technologies like contactless payments and GPS signals, emerging technologies like smart fabrics and sensors, and connected technologies like Bluetooth and the Internet of Things.

For over a decade, I’ve used these methods to teach designers how to explore sensors and prototype connected devices, without learning to code or becoming an electrical engineer, and now I’m doing the same thing for the current generation of AI.

What you’ll learn in this workshop

In this practical, fully-participatory, hands-on workshop, you’ll learn:

• the basic principles of conducting material exploration of technologies,
• communication strategies to share your learnings with non-design colleagues,
• end-to-end examples of AI/ML products and prototypes that demonstrate these practices in use,
• specific methods for material exploration of the current generation of AI, and
• how to support your design colleagues through critique.

You’ll get to practice everything you learn by conducting your own end-to-end material exploration of an AI technology, get peer critique, and report out your final learnings to the entire workshop.

For the duration of the workshop, you’ll have access to both the latest, long-context, large language models, and the latest diffusion models for multimedia generation, to ensure you’re getting to apply your learnings at the cutting edge.

What this workshop is not

This is not a workshop on ideating or brainstorming about the theoretical possibilities of AI/ML products; this is a workshop to ensure nothing you design is theoretical.

This is not a workshop on specific tools or services, on prompt engineering, model fine-tuning, or data preparation for ML ingest.

Workshop agenda

Day 1

Introduction and first exercise (60m)

• Introduction and level-setting
• End-to-end examples of AI/ML products and prototypes
• Group exercise: collecting and identifying AI/ML products

10m break

Tacit knowledge and desk research (60m)

• Group exercise: tacit knowledge about AI/ML products
• Group exercise: identifying underlying technologies

20m break

Material exploration and boundary objects (60m)

• Material exploration strategies (documentation, constraint exhaustion)
• Group exercise: planning a material exploration
• Communication strategies (boundary objects, dataflows)
• Recap and homework (project selection, planning)

Day 2

Homework review and setup (60m)

• Homework discussion and group assignments by project
• Introduction to available AI/ML tools
• Group work: material exploration plan

10m break

Material exploration of AI (60m)

• Group work: constraint exhaustion
• Group work: documentation
• Group work: critique

10m break

Boundary objects for AI (30m)

• Group work: design
• Group work: critique

10m break

Final presentations (30m)

• Presentation to workshop
• Recap

Target audience

Designers, researchers, product managers, and other non-engineering professionals who need to design an AI/ML hardware or software product, and who want to learn a framework for doing so.

Prerequisites

No technical experience necessary.

All AI/ML tools will be provided.

Full, active participation is expected throughout.

Non-designers without experience in the studio critique model of providing and receiving constructive feedback in the workshop setting will have pre-reading.

Takeaways

• An understanding of the methods and practices for material exploration of AI
• An understanding of boundary objects as the intermediary between your experience and your design, and the needs of your product and engineering colleagues
• A worked practice project

Contact me

If a colleague shared this with you, you can reach me via email: or text: .