Earlier this month, I had the chance to speak with students in the UXVDES 330 class at the University of Washington, diving deep into the future-forward integration of AI into UX research and design workflows.

The session, “Enhancing UX Research & Design with AI Tools,” was a fast-paced, exploratory conversation that unpacked both practical applications and paradigm-shifting opportunities for using AI in the UX process. The vibe was informal but packed with insight—fitting for a moment when design is being fundamentally redefined by generative tools and AI agents.

Shifting the UX Research Paradigm

I opened with a guiding belief: AI is not a shortcut to thinking—it's a new tool for thinking.

Using tools like Otter.ai, GPT models, and custom AI workflows, I demonstrated how user researchers can analyze interview transcripts, usability recordings, and survey data faster and more effectively. These tools aren’t just for summarizing—they’re helping teams surface patterns, generate personas, and synthesize pain points at scale.

Some highlights:

• A 30–50% reduction in research overhead is already being observed across organizations using AI tools for synthesis.

• AI augments human insight during data-heavy phases like affinity mapping and journey analysis.

• Custom GPTs built on internal data sources can increase confidence and reduce turnaround time.

Teaching Prompt Craft Like Design Strategy

One core focus of the talk was on prompting as a design discipline. I walked through an 8-part framework for crafting high-fidelity prompts that mirror UX outputs—journey maps, research plans, usability reports, and even epic-level stories in Agile workflows.

Prompting isn’t just about getting results—it’s about controlling tone, scope, constraints, and aligning AI with domain-specific context. I shared how I use structured prompting to simulate stakeholder interviews, create content outlines, and even build design system starter kits.

From Insight to Interface: AI Across the Double Diamond

The session moved across the UX double diamond:

• Discovery & Research: Use AI to synthesize interviews, extract trends, and validate assumptions.

• Definition & Exploration: Use AI to generate epics and user stories based on real pain points.

• UX/UI & Prototyping: Tools like Krea.ai and Midjourney helped create visual direction samples for healthcare ecommerce or arts-focused mobile apps.

• Testing: Create user testing scripts and plans that blend traditional usability with lightweight, AI-driven approaches.

Parting Thoughts

AI in UX isn’t just about speed—it’s about widening what’s possible. It’s an invitation to flex creative muscle, iterate faster, and stay focused on humans while navigating tech’s most disruptive shift in decades.

My advice to the students was simple: Stay flexible. Stay skeptical. But start now.

And yes—just get the monthly subscription. Something better is always coming next.