But most teams are still designing like it’s 2015.

AI is transforming industries and changing the very foundation of how products work.

Companies are moving fast to integrate large language models, agents, and adaptive systems into their products. But as model complexity grows, the user experience often gets left behind.

Powerful algorithms can now predict, generate, and decide but without thoughtful design, they often confuse users, introduce risk, or fail to earn trust.

The challenge?

Designing for AI isn’t like designing for traditional software.

It’s not just about usability or polish anymore.

It’s about system behavior, adaptation, and collaboration between humans and machines.

If you’ve ever launched an AI feature only to see users abandon it because they didn’t understand what it was doing or if you’ve been asked to “design the AI experience” but weren’t sure where to begin → this course is for you.

The Role of the Designer Is Changing

Until recently, designers focused on interfaces and flows. Today, the frontier is different.

OpenAI recently posted a job for a Model Designer, a new kind of design role focused on shaping how models behave.

And it signals a broader shift across the industry:

Designers aren’t just styling outputs anymore.

They’re architecting how intelligent systems learn, adapt, and interact.

In AI-powered products, design happens upstream, at the level of:

- Data inputs and how users shape them

- Model decisions and how they’re surfaced or corrected

- Feedback loops that guide learning

- System behavior that evolves over time

To adapt to this new environment, designers must understand how models work, not to become ML engineers, but to ensure what the system learns, shows, or hides aligns with human expectations and values.

Real Challenges This Course Prepares You For

You launch an AI-powered feature and maybe it recommends content, summarizes input, or automates a task. But users are confused or drop off quickly. They can’t tell why the AI did what it did, or how to influence it. The model works, technically. The experience doesn’t.

→ Learn to design transparency, feedback, and control into intelligent systems before trust is broken.

Your CTO asks you to “design the AI experience.” There’s no interface yet, just a prototype from the ML team and a prompt to “make it usable.” You’re left staring at system outputs, unclear where to start, or how much of the system behavior is even within your control.

→ Gain the tools and frameworks to work upstream, shaping behavior, not just UI.

You’re in a meeting where the team is debating whether to launch a model with 72% accuracy. Product wants speed. ML is confident the model will improve post-launch. But no one’s talking about the UX cost of being wrong nearly 1 in 3 times or how the system will recover from those mistakes.

→ Learn how to design for failure, uncertainty, and system correction from day one.

A personalization feature goes live. But user feedback is harsh: “This feels random.” You realize the issue wasn’t the model, it was the lack of communication and guidance. There’s no explanation, no context, and no way for users to improve the output.

→ Understand how to design model feedback loops and trust-building interactions.

You inherit a half-built AI assistant. It occasionally interrupts users at the wrong time, answers inconsistently, and disappears when needed most. The UI looks fine but the behavior feels off. You realize this isn’t a layout problem, it’s a systems design problem.

→ Move from UI design to shaping system behavior, flows, and agent logic.

You will bring your own real-life project scenarios, and we’ll coach you through how to solve them—using frameworks, feedback, and hands-on critiques.