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.
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.
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.
A study of Qwen's AI agent reveals 4 design lessons: support discoverability, reuse familiar patterns, handle personal data carefully, and protect user autonomy.
Hype happens when messaging becomes divorced from good experiences. Learning from the virtual reality hype cycle can help us avoid future distractions.
The NN/g UX Podcast featured Henry Modisett, head of design at Perplexity AI, who shared how to make AI tools usable and efficient for information seeking.
Applications native to smartwatch operating systems get used the most. Just because a new smartwatch app could offer basic functionality, it does not mean that users will find it valuable.
Smartwatches are for more than just receiving notifications and tracking steps. They afford at least 6 different types of interactions that users find useful.
Whether you’re running in-person or remote research on AR apps, ensure that the test is safe for study participants, the task wording is easy to understand, participants know what to expect in advance of the session, and your recording equipment can capture the participant’s screen and their movements.
Ecommerce AR tools are relatively new, so must be highly discoverable and easy to learn. Calibration issues run rampant, and users must dedicate focused attention to interact with this unfamiliar feature.
First-time users were concerned after using facial-recognition payment. Better onboarding experiences can relieve concerns and form factual mental models.