Nielsen Norman Group

Unsure where to start? Use this collection of links to our articles and videos to learn about how artificial intelligence works, and how users think about it.

How people react to being told that AI helped create a piece of content varies with audience, context, the nature and degree of AI use.

Unsure where to start? Use this collection of links to our articles and videos to learn about recommendations for designing AI products and features.

AI-agent power users curate 3 kinds of context: global (across tasks), local (task-specific), and ambient (raw streams like email).

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AI tools make it feasible to build fully interactive prototypes of complex interfaces so you can test them with users earlier in the design process.

NN/G uses AI for clarity, formatting, and critique, but humans retain editorial judgment and responsibility for every article.

Unsure where to start? Use this collection of links to our articles and videos to learn about the best ways to use artificial intelligence for UX work.

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Take up to 5 in-depth training courses, teaching user experience best practices for successful design. Training focused on long-lasting skills for UX professionals. September 14 - September 25, 2026.

AI can help you organize research you’ve already collected, but it can’t create research evidence about the specific, messy experience of real users.

AI lets teams build faster than UX can evaluate. UX can adapt by building shared judgment, accelerating evaluation, and guiding AI-generated designs.

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Plain-language definitions of the AI terms that come up in product and design work, from tokens and context windows to agents, evals, and prompt injection.

When users didn’t know whether an image was AI-generated, the images we tested did not create any perception penalty compared to stock photos.

Psychological ownership can make you more effective, but you’ll struggle if it attaches to things you can’t control. Spot misplaced ownership and redirect it.

One output cannot establish how well an AI system performs. Evaluate with multiple representative inputs, repeated runs, and confidence intervals.

Dogfooding, or using your own products internally, helps catch bugs, but it can't replace user research: your team knows too much to represent real users.

Pressure to adopt AI isn't evidence that a tool helps. The PROVE framework tests one tool against one task and produces a provisional decision you can defend.

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