Artificial Intelligence Articles & Videos

  • Why I’m Not Worried About My UX Job in the Era of AI

    AI is a new tool, not a replacement for UX professionals.

  • How AI Models Are Trained

    Training modern LLMs is a costly process that shapes the model’s outputs and involves unsupervised, supervised, and reinforcement learning.

  • CARE: Structure for Crafting AI Prompts

    To get better results from generative-AI chatbots, write CAREful prompts. Include context, what you’re asking the system to do, rules for how to do it, and examples of what you want.

  • Prompt Suggestions

    System-generated suggestions for AI prompts must be contextually relevant, personalized, and specific to the task and the user’s level of experience.

  • Synthetic Users: AI “Participants”

    Synthetic users are fake users generated by AI. While there may be a few use cases for them, user research needs real users.

  • Product-Specific GenAI Needs to Write for the Web

    Generative-AI outputs need to be concise, scannable, follow the inverted pyramid, and use plain language.

  • Your AI UX Intern

    Work with AI tools in UX the way you’d work with an intern: Treat their work as a first pass, double-check their facts, and provide specific instructions.

  • Four AI Superpowers: Where AI Improves Products

    When using AI consider its four "superpowers": content creation, summarization, basic data analysis, and perspective taking.

  • The Return of the UX Generalist

    AI advances make UX generalists valuable, reversing the trend toward specialization. Understanding multiple disciplines is increasingly important.

  • Discoverability of AI Features: Learn from Amazon’s Mistakes

    Even AI features that offer value won’t be used if people don’t notice them. Consider existing mental models and design best practices to increase engagement.

  • UX Leads Adoption of AI Chat

    UX ranks among top fields adopting AI, mostly in writing, design, and coding tasks — though complex or human-centric UX activities remain largely AI-free

  • Journey-Centric Design: The Evolution of Design Ops

    Product-centric design does not leverage design’s potential for creating long-term business value and profitability. Journey-centric design can activate this potential and optimize customer experiences.

  • AI Adoption in the Workplace Still Low, 2 Years Later

    Designers may think AI features are now familiar to our users, but recent research suggests that adoption is still lower than we might think.

  • Scope in Generative AI Features

    When designing AI product features, scope shapes key decisions. Research shows that focused AI features lead to better user understanding and adoption.

  • Common-Sense AI Integration: Lessons from the Cofounder of Condens

    Experience in the context of a UX-research platform shows that AI can be integrated well in focused tasks but isn’t capable of independent complex analysis.

  • How Service Design Will Evolve with AI Agents

    AI will force the transformation of service design by introducing new actors, shifting user dynamics, and redefining success metrics.

  • AI Hallucinations: What Designers Need to Know

    Plausible but incorrect AI responses create design challenges and user distrust. Discover evidence-based UI patterns to help users identify fabrications.

  • Promptframes: Using AI-Content to Inspire Feedback

    Promptframes combine traditional wireframes with AI prompts to create content-rich prototypes. Ditch confusing placeholders using AI to quickly inspire feedback from your collaborators and users.

  • The UX Reckoning: Prepare for 2025 and Beyond

    In 2025, reevaluate tactics; use AI to deliver user value; and develop deep UX skills, instead of relying on toolkits.

  • Redefine Your Design Skills to Prepare for AI

    Designers must embrace 5 principles as our industry shifts with AI.