As AI reshapes product design, it could give designers greater autonomy or expose the gaps that autonomy makes harder to hide. Exploring both the bull and bear cases, Andy Budd examines what happens when designers need less permission to act.
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Many of the AI tools we interact with take the form of text boxes. But what if there was a different way to interact with AI? Oleksii Hrzhehorzhevskyi explores a different approach to creating a new AI assistant and how designers can navigate the field as AI continues to change it.
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Many companies assume everyone craves new AI features. But the reality is that most people don’t want more AI — at least not in the way most AI leaders envision it. Brought to you by Design Patterns For AI Interfaces, friendly video courses on UX and design patterns by Vitaly.
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Many UI trends are designed to capture attention and signal innovation, but those goals often conflict with the needs of mental health apps: reducing cognitive strain, fostering trust, and providing a sense of refuge. Kat Homan introduces an evaluation framework that helps designers assess whether trendy visual and interaction patterns support or undermine the unique goals of mental health experiences.
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A closer look at why users don’t need more tools in their daily lives. What they need are seamless integrations of useful features to match already existing, established mental models. Brought to you by Design Patterns For AI Interfaces, friendly video course on UX and design patterns by Vitaly.
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We’ve fallen into conversational tunnel vision, defaulting every AI capability into a chat-based interface simply because LLMs are trained on dialogue data. But great UX is about matching modality to users’ context, intent, and cognitive load, so the interface adapts to the user, not the other way around.
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Teams can generate UI faster than ever, but they still have to guarantee that what they ship is usable, secure, and maintainable. Accessibility as an operational capability rather than a compliance checklist or end-of-project audit, and what that looks like in practice.
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In a world where AI is informing more design choices, it’s easy to mistake predictions for certainties. This article introduces Probabilistic Design, a mindset that allows UX and product teams to accept uncertainty, decipher AI outputs with nuance, and make smart, adaptive decisions.
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Findings from an exploratory user research study highlighting the unique insights and practical UX recommendations shared by participants with cognitive disabilities.
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Practical guide on how to reduce drifts, minimize mistakes, maintain context, and improve the quality of AI-generated prototypes. Brought to you by Design Patterns For AI Interfaces, friendly video course on UX and design patterns by Vitaly.
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