AI for UX & UI Design

Learn how to integrate AI into the UX design process while maintaining strong design judgment, accessibility, and user control. In this advanced three-day course, you’ll build a reusable AI-powered UX workflow that supports research, product thinking, design, prototyping, and evaluation.

Working with a focused sample project, you’ll create an AI workspace that grows with your project, use AI to organize and analyze research, define an appropriate AI-powered experience, design an inclusive user flow, and turn your designs into a working prototype. You’ll also test realistic scenarios, evaluate AI-generated outputs, and refine both your design and AI instructions based on what you learn.

Rather than relying on one specific AI model or tool, the course focuses on transferable workflows and responsible AI use that can be applied with tools such as Claude Code, OpenAI Codex, Figma Make, or other AI development environments.

What You Will Learn

• Build a reusable AI workspace that supports your UX process from research through prototyping and testing
• Use AI to organize research, identify patterns, and develop evidence-based product opportunities
• Turn research findings into problem statements, user journeys, user stories, and feature priorities
• Define when AI should act, assist, recommend, request approval, or leave control with the user
• Design inclusive AI-powered experiences that account for accessibility, uncertainty, errors, correction, and recovery
• Turn structured Figma designs into interactive prototypes using AI-assisted development tools
• Test AI-powered experiences using realistic situations, including incomplete information and incorrect or uncertain outputs
• Evaluate and refine AI-generated work for design quality, accessibility, consistency, trust, and user control
• Create a repeatable Agentic AI workflow that can be applied to future UX projects

Course Outline

Day 1: Build Your AI Brain

• Capture your UX process, methods, standards, project goals, audience, constraints, measures of success, and expectations for how AI should support your work in one shared workspace.
• Teach an AI agent how you approach research, design, testing, and decision-making, and create a system that improves as new project information is added.
• Plug your Skills.MD into real research tasks and guide AI to generate research plans, questions, and hypotheses.
• Use prepared competitive research, behavioral research, survey results, and interview notes to organize information, identify patterns, and suggest findings and opportunities.
• Compare AI outputs with the original research, remove exaggerated, unsupported, or overly general findings, and refine instructions when the agent makes assumptions.
• Develop a clear project brief, working AI Brain guidelines, and research findings to guide the next phase of the project.

Day 2: Design the Product and How AI Should Help

• Use your AI Brain to generate problem statements and opportunity areas, map user journeys, and turn research findings into user stories and feature priorities.
• Choose one focused product opportunity and define where AI should act, assist, recommend, ask for approval, or leave control with the user.
• Plan human review, approval, correction, and intervention, including smooth handoffs between AI support and human support.
• Design an experience that helps users understand what AI is doing and accounts for consent, correction, undo, uncertainty, errors, and recovery.
• Develop a mid-fidelity user flow that is inclusive, accessible, understandable, and grounded in the research findings.
• Review the flow for accessibility, inclusive design, trust, and user control, then add the resulting product direction, AI responsibilities, interaction patterns, and accessibility guidelines to your AI Brain.

Day 3: Turn Your Design into a Working Prototype

• Prepare clean, structured Figma files with reusable components and consistent styles for AI-assisted development.
• Use tools such as Figma Make, Claude Code, or Codex to turn designs into an interactive prototype using project goals, research findings, user needs, accessibility requirements, design guidelines, and expected AI behavior.
• Review the AI agent’s proposed approach before building, then compare the generated prototype with the intended experience for design quality, accessibility, and consistency.
• Define success criteria and create realistic testing situations involving successful tasks, incomplete information, uncertain AI responses, incorrect results, human approval, correction, and recovery.
• Evaluate clarity, accessibility, trust, and user control, and determine whether issues should be corrected in the interface, user journey, AI instructions, accessibility guidance, project information, or testing criteria.
• Update the appropriate parts of your AI Brain and prototype based on testing results, then test the experience again.

Independent Student Project

• Define a product challenge and audience, build and customize your own AI Brain, and create a focused research plan.
• Gather and organize supporting evidence, analyze the research, and identify product opportunities.
• Develop problem statements, a user journey, and user stories, and define how AI should support the experience and where human controls are needed.
• Create a working prototype and vibe-code a React or Next.js prototype using design-system context.
• Test realistic situations and AI responses, including variable AI outputs, and document improvements and design decisions.
• Turn the completed work into a portfolio-ready case study.