Context architecture applies information architecture principles to AI systems, helping agents interpret information and produce better, user aligned responses.
To build useful and usable AI-powered systems, our understanding of users’ needs and our design judgement must be encoded into well-defined evaluation criteria.
A study of Qwen's AI agent reveals 4 design lessons: support discoverability, reuse familiar patterns, handle personal data carefully, and protect user autonomy.
Helpful site-specific AI chatbots clearly state their capabilities, offer relevant prompt suggestions, and quickly signal they know what users are looking at.
Users turn to site-specific chatbots for quick answers, not a conversation. Design responses that are direct, scannable, and easy to expand when needed.
AI agents now interact with digital interfaces alongside humans. Designing for both requires rethinking what "user" means and prioritizing accessibility.
An AI agent pursues a goal by iteratively taking actions, evaluating progress, and deciding next steps. Useful agents must be reliable, adaptive, and accurate.
AI can produce polished survey drafts quickly, but experienced human review is still needed to catch subtle survey-design flaws that weaken data quality.
With generative UI, the AI system decides to generate an interactive element or entire product in response to a user need. Vibe coding is when users request the AI to build it.
UX faced instability in 2025 from layoffs, hiring freezes, and AI hype; now, the field is stabilizing, but differentiation and business impact are vital.