Nielsen Norman Group

Live UX Training for APAC Time Zones. No 2 AM alarms required! ⏰ In November, we’re staying up late, so you don’t have to. It’s tough to attend live training when the sessions run in the middle of your night 😴, so we’ve added dedicated course times to our November training schedule optimized for Asia, Australia, and Pacific time zones. 📍 Singapore: 9:00 AM 📍 Seoul/Tokyo: 10:00 AM 📍 Sydney: 12:00 PM 📍 San Francisco: 5:00 PM (previous evening) Live UX training at times that fit your region’s workday: ✅ 100% live, interactive courses ✅ Hands-on exercises ✅ Expert instructor Q&A ✅ Credit toward your UX Certification Each Course is: 2 days | 3.5 hours/day | 100% Live & Interactive Check the 8-course schedule and look up the start time in your city. 🔗 https://bit.ly/4rGF5U0 #UXTraining #UXCourses #AICourses #UXUI

Telling your audience that content was created with AI can build trust.🛡️ Or… it can backfire completely.🔥 Our research shows that user reactions to AI disclosures aren’t uniform. How people respond depends on who they are, where they see the content, and how heavily AI was involved in its creation. Disclosure isn't one-size-fits-all. It's a contextual design decision. To help teams decide when, where, and how to disclose AI use, NN/G developed the PACED Framework: P = POLICY: Are there legal, organizational, or platform rules requiring disclosure? A = AUDIENCE: How will your specific users perceive AI involvement? Will it build trust or breed skepticism? C = CONTEXT: Where is this content appearing, and what level of transparency do users expect in that space? E = EXPECTATIONS: Would your readers feel misled if they later learned that AI was involved? D = DEGREE OF USE: Was AI used for minor formatting, or did AI generate the core idea? Slapping a blanket “AI-generated” disclaimer on everything isn’t always the best approach, but as disclosure requirements evolve, organizations will have to find ways to evaluate the trade-off between transparency and potential reputational risk. ✅ The PACED Framework turns complex disclosure decision making into a set of questions you can apply to your own content, audience, and context. 👉 Read “When Should You Disclose AI Use? The PACED Framework” to understand how our research can help inform your AI disclosure decisions: https://bit.ly/4rVPBXN 💬 How is your team handling AI disclosures with your audience? Share your approach below. #UXDesign #UXResearch #ContentStrategy #DesignOps #AIinDesign

Planning next year’s UX-team training budget? Make every dollar go further. Training budgets are tightening, while UX and product teams are being asked to build new skills, especially around AI. NN/G’s Group-Discount Pricing bridges that gap. When you purchase 30+ live online training courses, you can: → Save up to 39% on courses and up to 47% on UX Certification exams (thousands of dollars) → Give your team more time to use your budget, with up to 18 months to complete training courses, depending on your program → Let team members choose the courses that fit their roles and skill gaps, from UX fundamentals to AI for research, design, product strategy, and more → Track requests, usage, remaining courses, and UX Certification progress in one place Lock in your group-discount pricing now and give your team the flexibility to learn what they need, when they need it most, throughout the year. Explore group-discount pricing: https://bit.ly/4dNNMGo #UXTraining #UXLeadership #AI #ProfessionalDevelopment #NNG

A single successful AI response isn't proof. It’s just an example. Decades of traditional software taught us that if a feature works once, it works every time. AI broke that rule. Because language models are non-deterministic, submit the exact same prompt twice and you might get: → Run 1: A precise summary of your return policy. → Run 2: A confident promise of a refund the customer isn't entitled to. One great output proves your system CAN perform a task—not that it will do so reliably. Adapting to this shift doesn't require inventing a new measurement science. It requires classic quantitative UX research: ➡️ **Test edge cases:** Easy inputs give you precise answers to the wrong questions. ➡️ **Run inputs repeatedly:** Repetition is the only way to separate a reliable system from a lucky run. ➡️ **Report averages with confidence intervals:** A single score is an estimate, not a guarantee. ➡️ **Don't let cost compromise sample size:** Truncating test runs to save money leaves you with a cheap estimate, not statistical proof. Nondeterministic systems require statistical evaluation, not spot checks. In her latest article for NN/G, Raluca Budiu breaks down why decades-old research principles apply directly to modern AI evaluation: "One AI Output Is an Example, Not an Evaluation." ➡️ https://bit.ly/4z9bsNA #AI #UXResearch #QuantitativeResearch #ProductManagement #AIEvaluation

UX has been disrupted before. Kara Pernice was on stage at DesignUp 2026 in Bengaluru with lessons from those earlier waves for the AI moment. If you're there, go say hello.

Most teams treat journey maps and service blueprints as if they’re synonyms. They dump front-stage user pain points and backstage technical dependencies onto the same board, end up with a tangled mess, and wonder why leadership tunes out. These tools aren't interchangeable. They're sequential. 👉 You map the customer journey first. Then, build the service blueprint to fix the operational machinery behind it. ➡️ JOURNEY MAPS: Center on the customer's reality — what they do, think, and feel at every touchpoint. ➡️ SERVICE BLUEPRINTS: Center on your organization's reality — the backstage processes, systems, and personnel required to support that customer. ⚠️ If you map backstage operations before you truly understand the user’s experience, you just end up optimizing an operationally efficient way to disappoint people. Use a JOURNEY MAP to: → Uncover user behaviors, thoughts, and feelings → Build team alignment around actual user pain points → Identify experience gaps to prioritize what to build next Use a SERVICE BLUEPRINT to: → Align crossfunctional teams around a single source of truth → Map backstage systems powering each touchpoint → Diagnose operational breakdowns and assign clear ownership A service blueprint is only as good as the journey map that precedes it. If you want your blueprints to work, you have to master the user journey first. Learn how to build journey maps that drive real organizational action in our self-paced course → https://bit.ly/4dVBIms #UXDesign #ServiceDesign #CustomerJourney #UXStrategy #UserResearch

With AI, everybody’s a designer now. PMs are prompting UI mockups. Engineers are asking coding assistants where to place buttons and how to word error messages. Designers are speeding up wireframing with generative tools. And the output? Meh. The exact same, middle-of-the-road UI. AI models know what software generally looks like. But what they don't know is your specific users, your domain terms, or your team's deeply researched usability standards. Without that context, the output is always going to be the internet's average. The immediate instinct is to write a "better" prompt. But you can tweak your prompt language all day long and it won't rescue a generic design. The teams getting real results from AI tools aren't perfecting prompt engineering. 🎯 They’re practicing UX context engineering. The primary consumer of your UX deliverables is changing from humans to AI. Which means the core role of UX is shifting from document creator to context architect. Instead of writing research documents for human archives, UX context engineers are translating user knowledge into machine-readable context that guides every AI tool across the org. Swipe through to see how this critical UX skill can elevate your team's impact 👇 Join NN/G on December 14 & 15 for the debut of our live online course: Context Engineering: Shaping AI-Generated Designs to learn how to turn traditional deliverables into structured context sets that scale usability standards across product teams. https://bit.ly/4ypfZLY #UXDesign #ContextEngineering #DesignSystems #ProductManagement #AIDesign

When you’re performing a usability test, are you thinking about how much information is being exchanged? A moderated usability test is a web of information involving 3 parties: → The researcher → The participant → The tasks In this sort of setup, every movement by the researcher and every comment by the participant shape how the session flows. Participants are noticing how you’re observing them, and you’re noticing how they’re performing their task. If it’s been a while or this feels new to you, it might be time to refresh usability-testing essentials with our Usability Testing 101 article. https://bit.ly/46JAAP2 #UXResearch #UX #UXUI #UsabilityTesting

From the early days of the web to the mobile revolution and through the transition to Lean/Agile; UX has faced major technological changes before. Each time a new technology arrives, it can feel like a threat. But looking back through decades of UX archives reveals a clear pattern: **Every major evolution starts as a threat of uncertainty before becoming our next great opportunity.** AI is no different. Our President & CEO, Kara Pernice, will be taking the stage at the DesignUp™ Conference in Bangalore, October 1–4. In her talk, "UX and Disruption Déjà Vu," she’ll explore how historical patterns in technological disruption can guide us through the current AI era. Will you be there? #UXHistory #TechnologicalDisruption #Reinvention #UX

If you missed our latest webinar, don’t worry. We recorded it, so you can still learn the vital AI evaluation framework PROVE: https://bit.ly/4rxywmR To help you get started, we’ve created an interactive workbook to walk you through the steps that help you decide which tasks need an AI tool. Want more structured guidance? The full course will walk you through the guide's instructions and show you the other key steps for successful AI evals: https://bit.ly/4iTViDf In the meantime, explore the workbook: https://bit.ly/47iFN0p #UXTraining #UXAI #AIEvals #FreeWebinar