UX Research Certification in AI: Three Courses, One Applied Capstone.
A three-course program with a capstone that teaches UX researchers how to design and implement a powerful AI strategy across a research practice.
- Investment
- $1,485
- Format
- 3 live courses + capstone
- Structure
- Any order · ~15 weeks combined
Choose your cohort for each course below, then use this button — or enroll in each course individually. Enter code [NEED: coupon code] at checkout for $200 off all three.
Lifetime access to all three courses
Every recording and resource across all three courses is yours to keep.
Each course's own refund policy applies
Full refund on any course up to its start date. No bundle lock-in.
UX Research Certification in AI
A shareable credential once you've completed all three courses and the capstone.
The question isn't whether you use AI in your research. It's whether you use it well.
Adding AI to your practice isn't the hard part; a prompt and an afternoon will get you most of the way. The hard part is integrating it so your rigor survives the speed. Done haphazardly, AI erodes the things that make research trustworthy: traceability, defensible analysis, a method you'd stand behind in a readout. Done deliberately, it makes all of that faster without giving any of it up. This certification is built around that difference. It is about processes, not just the tools.
And it covers both sides of where AI meets your work. Two courses focus on using AI inside your own practice, accelerating analysis and building infrastructure that outlasts a single project. One focuses on the other direction: studying AI products as the subject of research, since more and more of what you're asked to evaluate is itself an AI feature. The capstone brings them together in one applied project, so you leave having built something applicable to your work.
Complete all three. Any order.
Two courses teach you to use AI inside your research practice. One teaches you to research AI products themselves. Enroll in whichever fits your calendar first, or use the bundle above to get all three at once.
Using AI Responsibly for Faster + Deeper Insights
Build an intentional, AI-supported qualitative workflow without trading away rigor.
Beyond Prompting: Building AI Infrastructure for UX Research
Build reusable context systems, research memory, and workflow libraries that outlast any single project.
UX Research for AI Products: Methods for a Moving Target
Diagnose what's different about AI features, and design studies for unpredictable, trust-sensitive products.
What it takes to earn the certification
- 1
Take the 3 required courses in any order
- 2
Submit the capstone project, the AI Implementation Plan, within two months of completing your last course. All three instructors review it and give you feedback.
- 3
Receive capstone feedback + approval
The capstone: your course work, compiled and reviewed.
The AI Implementation Plan isn't a new project. It's the output you already produced across the three courses: your infrastructure plan from Beyond Prompting with your analysis approach from Using AI Responsibly layered in, plus your adapted research templates from UX Research for AI Products, compiled into one package.
You add one thing on top: a brief implementation case study showing how you actually used what you built, a short write-up, a demo, screenshots, or a workflow diagram, whatever's fastest for you to put together. All three instructors review it and give you direct feedback, not just a pass/fail stamp. You have two months after finishing your last course to submit.
- 01
Apply AI responsibly to accelerate qualitative coding, synthesis, and insight generation, using AI inside your own research process.
- 02
Build reusable AI-supported research infrastructure, context systems, and workflow libraries.
- 03
Design and evaluate UX research studies for AI-powered products, treating AI as the subject of research, not just a tool.
- 04
Adapt standard research planning and execution documents, screeners, discussion guides, analysis plans, for the unpredictability of AI products.
- 05
Compile an AI Implementation Plan from your own course work that demonstrates both directions together: your own AI-supported workflow and research designed for an AI product.
How will the UX Research Certification in AI help your career?
Lead AI adoption decisions in your research org
When leadership asks how the research function should use AI, you'll have a structured answer spanning analysis, infrastructure, and AI-product methods, not just opinions about one tool.
Develop competency in both directions of AI and research
Most researchers pick up AI skills from only one side: tool fluency, or exposure to one AI-product study. This program gives you both, accelerated analysis and durable infrastructure for your own practice, plus the methods to study AI products the moment your team ships one.
Demonstrate applied AI research expertise
The AI Implementation Plan gives you a real, defensible artifact for interviews, portfolio reviews, and promotion conversations, not just a certificate line that says "AI training."
Achieve methodological authority on AI in your organization
After this program, you're equipped to set the standard for how your team uses AI in research, including where it helps and where it introduces risk.
Strengthen credibility across data science, product, and leadership
Three instructors, three domains, one throughline: you become the researcher who can speak to AI-assisted analysis, infrastructure decisions, and AI-product research in the same conversation.
→ Built for you if
- You're an intermediate-to-advanced UX researcher who wants a structured, comprehensive AI credential covering both AI infrastructure for research and the methodological adaptations needed to study AI.
- You want a portfolio-ready capstone project you can point to in interviews or promotion conversations.
- You're ready to commit to three courses across several months, on your own schedule.
× Not the right fit if
- You're new to UX research and haven't run studies independently yet. Start with foundational research training, then come back.
- You just want to try AI in research once before committing to anything. Start with the free mini-course instead.
- You want to focus on using AI for specific research tasks, not the full practice. Take Using AI Responsibly for Faster + Deeper Insights on its own.
Assumed going in
- You've independently planned, run, and reported on UX research studies.
- You're comfortable working without step-by-step AI tool tutorials. This program teaches judgment, not software.
- Everyday familiarity with at least one AI assistant (ChatGPT, Claude, or similar).
Leo Hoar, PhD
Leo founded the UXR Institute after seeing UX research's power to help businesses make smart decisions, and built it to make substantive UXR expertise easier to exchange across the community. His course teaches how to take the grind out of first-pass coding with AI without flattening the meaning, freeing you for the interpretive work only a human can do.
Full bio
Aaron Kagan, PhD
Aaron has spent the better part of a decade doing the definitional work that comes before an AI product can be evaluated at all, including building Google's Taxonomy of Social Benefits in AI and the standards the company uses to prevent AI products from being anthropomorphized. He is the author of An Introduction to Embodied Mind (Routledge, 2026).
Full bio
Victor Yocco, PhD
Victor has spent more than 15 years in UX research, the last three focused on the psychology of human-AI interaction. His current research centers on the use and adoption of AI in enterprise software used by millions daily, and he consults with large organizations on the behavioral barriers that slow AI uptake among high-performing teams.
Full bioEach course runs its own live cohort on its own calendar. There's no requirement to take them back to back; enroll in each when it fits, or use the bundle to lock in all three.
Live, hands-on sessions with practice between them, per each course's own syllabus.
Compile it from what you already built in the three courses, plus a brief case study on how you implemented it. Submit within two months of finishing your last course. All three instructors review it and give you direct feedback.
Where the field is heading
Three pieces we keep coming back to on what an AI-native research practice actually looks like.
-
Frontier UX Research Circa May 2026
Jess Holbrook
Holbrook argues that becoming AI-native is about redesigning how evidence flows through an organization, not just adopting new tools. He lays out three shifts that have to happen together: an AI-native mindset, a new operating model where humans supervise while AI handles the repeatable work, and infrastructure that connects research to decisions. The piece pairs concrete frameworks and early "green shoots" with honest cautions for teams trying to move toward frontier work.
-
Trade Your Double Diamonds for Steel
Erika Flowers
Flowers makes the case that AI collapses the old split between discovery and delivery, fusing them the way iron and carbon fuse into steel. Instead of moving through the separate phases of the Double Diamond, she argues, thinking and making now happen at once in a continuous forge. Her warning is against simply speeding up existing workflows, and her invitation is to work in a genuinely new material.
-
Shift Center
Maya Elise Joseph-Goteiner
Joseph-Goteiner argues that the familiar advice to "shift left" no longer fits product development that has compressed into one continuous flow without clean phases. She offers "Shift Center" instead, urging research leaders to build organizational infrastructure for continuous learning rather than fight for a seat at the table. As AI drives down the cost of producing insight, the real value moves to architecting trustworthy knowledge systems and governance.
What is the UX Research Certification in AI, and what do I get? +
The UX Research Certification in AI is a UXR Institute credential built from three live courses, taken in any order, plus an applied capstone project. It's the only UX research credential covering both directions of AI and research: using AI inside your own research practice (faster analysis, durable infrastructure) and researching AI products as the subject of study. You'll complete Using AI Responsibly for Faster + Deeper Insights, Beyond Prompting: Building AI Infrastructure for UX Research, and UX Research for AI Products: Methods for a Moving Target, then submit an AI Implementation Plan that proves you can do both. You finish with a Certificate of Completion and a portfolio-ready artifact, not just three separate course certificates.
Can I take the three courses in any order? +
Yes. The three courses don't depend on each other, so enroll in whichever fits your calendar first.
How is this different from taking Using AI Responsibly for Faster + Deeper Insights on its own? +
Using AI Responsibly for Faster + Deeper Insights teaches one piece of the practice: accelerating qualitative coding and synthesis with AI. On its own, it doesn't cover building infrastructure that survives beyond a single project, and it doesn't touch researching AI products themselves. The certification requires all three courses plus a capstone that applies them together, so it certifies the full practice, not one technique. If you only need faster analysis right now, the standalone course is the right call; if you want the credential and the portfolio artifact, you need the full program.
What is the capstone project? +
The AI Implementation Plan isn't a new project. It's the output you already produced across the three courses: your infrastructure plan with your analysis approach layered in, plus your adapted research templates, compiled into one package. You add a brief implementation case study on top, a short write-up, a demo, screenshots, or a workflow diagram, showing how you actually used what you built. All three instructors review it and give you direct feedback, and you have two months after finishing your last course to submit.
Do I need to enroll in all three courses at once? +
No. Enroll in each course as its cohort comes up, or use the "Add All 3" button to add all three at once, then enter the certification coupon code at checkout for $200 off.
Is a UX research certification worth it? +
It depends on what you need. If you want proof you can run a defined, multi-part practice, not just that you sat through one workshop, a structured certification with a real deliverable carries more weight in a portfolio review or promotion conversation than a single course certificate. This one is built specifically around the applied capstone rather than certifying attendance.
Do I need prior AI experience to start? +
No prior AI or technical expertise is required. You do need to already be comfortable running UX research independently; the three courses assume research fluency and teach the AI layer on top of it.
What will I be able to do after completing the certification? +
You'll be able to accelerate qualitative analysis with AI without losing rigor, build reusable AI-supported research infrastructure that outlasts a single project, design and evaluate research studies specifically for AI-powered products, and adapt your standard planning and execution documents for AI products' unpredictable behavior. The capstone requires demonstrating all of this together, not just recognizing it in a lecture.
Is this useful for PMs and designers, or only researchers? +
The certification is built for UX researchers. Some content, especially the AI infrastructure course, is useful to research-adjacent PMs and designers who work closely with a research function, but the prerequisites and capstone assume you're the one running the studies.
Can I expense this? Is it worth the cost to my team? +
Most participants expense it as professional development; we can provide an itemized receipt on request. Framed to a manager, it's a structured, multi-month credential with a real deliverable, an AI Implementation Plan your team can actually use, not a one-off workshop.
What's the real time commitment across all three courses? +
Combined, the three courses run about 15 weeks of live cohort time, though you don't have to take them back to back. Each course has its own weekly time commitment, detailed on its own course page. The capstone isn't a new time investment on top of that, it's compiled from work you already did, plus a brief write-up, and you have two months after your last course to submit it.
What if I'm not sure AI in research is right for me yet? +
Start with Analyze Qualitative Data with AI, a free, self-paced mini-course. If the workflow clicks, the certification is a natural next step. If it doesn't, you haven't spent anything finding out.
Try it before you commit.
Not everyone arrives ready to commit to a three-course, $1,485 certification, especially if you haven't used AI in your research practice yet. Analyze Qualitative Data with AI is a free, self-paced mini-course, nine lessons that walk you through one structured AI-assisted analysis workflow end to end. If it clicks, the certification is the natural next step. If it doesn't, you've lost nothing.
Start the free AI for Qualitative Data Analysis courseEnroll with confidence. Every course in this program carries its own full refund policy up to its start date, no bundle lock-in. Lifetime access to every recording is yours to keep as soon as a course begins.
Build the AI research practice your team is already asking you for.
$1,485 for all three courses · $200 off with code [NEED: coupon code] at checkout · individual course cards above if you'd rather enroll one at a time
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