AI in UX: Teaching the Next Generation of Designers at UW
From research synthesis to design prototyping, AI is redefining how we teach and practice UX
Earlier this month, I had the opportunity to guest lecture in the UXVDES 330 class at the University of Washington, leading a session called “Enhancing UX Research & Design with AI Tools.”
The session was fast-paced, exploratory, and centered on a core belief: AI is not a shortcut to thinking—it’s a new tool for thinking.
AI as a Research Multiplier
I demonstrated how AI-powered tools—such as Otter.ai, GPT models, and custom workflows—are reshaping UX research. These tools don’t just summarize; they synthesize: surfacing patterns, generating personas, and accelerating affinity mapping.
Organizations experimenting with AI in UX research are already reporting:
30–50% reduction in research overhead
Faster synthesis of transcripts, surveys, and usability feedback
Improved confidence when using custom GPTs trained on internal data
Rather than replacing researchers, AI augments their capacity, making insight-gathering faster and more actionable.
Prompting as a Design Discipline
A central theme of my lecture was the idea of prompting as design strategy.
I shared an 8-part prompting framework for aligning AI outputs with real UX artifacts—journey maps, research plans, usability reports, even Agile epics. Prompting becomes less about “getting results” and more about:
Defining scope and tone
Controlling constraints
Embedding domain-specific context
The outcome: high-fidelity outputs that mirror the rigor of traditional UX methods.
AI Across the Double Diamond
We explored AI’s role across the full UX double diamond:
Discovery & Research → Synthesizing interviews and extracting trends
Definition & Exploration → Generating user stories and epics from pain points
Design & Prototyping → Using tools like Krea.ai and Midjourney to develop visual directions
Testing → Drafting usability scripts that blend traditional and AI-driven methods
At each phase, AI accelerated progress while keeping human judgment and creativity at the center.
Final Thoughts
AI in UX isn’t just about speed—it’s about expanding what’s possible. For the students, I stressed three things:
Stay flexible. Tools will keep changing.
Stay skeptical. Treat AI as an assistant, not a truth source.
Start now. The future of design is already being written.
This is the most disruptive shift in decades for our discipline. If we approach AI thoughtfully, it won’t just help us design faster—it will help us design better.
Earlier this month, I had the opportunity to guest lecture in the UXVDES 330 class at the University of Washington, leading a session called “Enhancing UX Research & Design with AI Tools.”
The session was fast-paced, exploratory, and centered on a core belief: AI is not a shortcut to thinking—it’s a new tool for thinking.
AI as a Research Multiplier
I demonstrated how AI-powered tools—such as Otter.ai, GPT models, and custom workflows—are reshaping UX research. These tools don’t just summarize; they synthesize: surfacing patterns, generating personas, and accelerating affinity mapping.
Organizations experimenting with AI in UX research are already reporting:
30–50% reduction in research overhead
Faster synthesis of transcripts, surveys, and usability feedback
Improved confidence when using custom GPTs trained on internal data
Rather than replacing researchers, AI augments their capacity, making insight-gathering faster and more actionable.
Prompting as a Design Discipline
A central theme of my lecture was the idea of prompting as design strategy.
I shared an 8-part prompting framework for aligning AI outputs with real UX artifacts—journey maps, research plans, usability reports, even Agile epics. Prompting becomes less about “getting results” and more about:
Defining scope and tone
Controlling constraints
Embedding domain-specific context
The outcome: high-fidelity outputs that mirror the rigor of traditional UX methods.
AI Across the Double Diamond
We explored AI’s role across the full UX double diamond:
Discovery & Research → Synthesizing interviews and extracting trends
Definition & Exploration → Generating user stories and epics from pain points
Design & Prototyping → Using tools like Krea.ai and Midjourney to develop visual directions
Testing → Drafting usability scripts that blend traditional and AI-driven methods
At each phase, AI accelerated progress while keeping human judgment and creativity at the center.
Final Thoughts
AI in UX isn’t just about speed—it’s about expanding what’s possible. For the students, I stressed three things:
Stay flexible. Tools will keep changing.
Stay skeptical. Treat AI as an assistant, not a truth source.
Start now. The future of design is already being written.
This is the most disruptive shift in decades for our discipline. If we approach AI thoughtfully, it won’t just help us design faster—it will help us design better.
Listen On Spotify
Listen On Apple Music
