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UX for AI: Design Practices for AI Developers

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AI developers are tasked with addressing a wide variety of complex challenges. They’re customer facing, too, which means their UX design interfaces need to be both ethical and effective. In this course, award-winning designer and engineer John Maeda shows you how to navigate UX design for AI applications using a thoughtful balance of design practices that leverage the power of AI models while also addressing user needs and expectations.

From designing for trust to testing and iterating, error handling, recovery, and ethics, John helps you get up to speed with new approaches to collaborative UX with tools and frameworks like the Microsoft Copilot stack, Semantic Kernel, and more. Learn how to define clear goals to guide UX design decisions and help prioritize features and functionalities. Along the way, get tips for maintaining transparency so users can interact with AI-powered apps that don't feel like a black box.


01 - Introduction
  • 01 - Designing for AI
  • 02 - What is copilot stack

  • 02 - 1. Collaborative UX in The AI Copilot Stack
  • 01 - What is collaborative UX
  • 02 - Collaborative UX practices
  • 03 - Copilot stack layers

  • 03 - 2. The Titanium Tetrahedron in AI UX Matters
  • 01 - The three iron triangle dimensions
  • 02 - Dimension 1 Practices for speed
  • 03 - Semantic coding for speed
  • 04 - Dimension 2 Practices for costs
  • 05 - Semantic coding for cost
  • 06 - Dimension 3 Practices for quality
  • 07 - Dimension 4 Practices for emotion
  • 08 - Semantic coding for emotion

  • 04 - 3. Building Appropriate Trust
  • 01 - Adding friction The new math
  • 02 - Whats responsible technology
  • 03 - Semantic coding for responsible AI

  • 05 - 4. Conclusion
  • 01 - Next steps
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