Human-Centered AI: Designing Trustworthy Experiences
Learn AI design principles to create user-centered, trustworthy, and effective AI experiences.
About the course
A closer look at the course
AI is transforming digital products, but the real challenge is not the technology. It is still about solving the puzzle of human behavior and needs.
This course teaches you how to design AI experiences that meet real user needs. Whether you are a designer, product manager, developer, or researcher, you will learn what makes AI valuable instead of just impressive.
AI learns, adapts, and makes probabilistic decisions. AI sometimes makes mistakes, and this forces us to rethink how we design. You will explore methods for building trust, handling uncertainty, and making AI behavior clear to users.
Through practical frameworks and examples, you will learn when AI creates value and when simpler solutions are more effective. You will design feedback systems, create mental models that help users, and build products that keep humans in control.
The curriculum covers key areas such as assessing user needs, defining success metrics, designing transparent interactions, and managing errors in a graceful way. You will also discover how to guide AI so it develops responsibly and continues to serve users.
From chatbots and assistants to recommendations and predictions, the principles you will gain apply across AI products. By understanding both the strengths and the limits of AI, you will design experiences that support human abilities and earn user trust.
User Needs and AI Value Mental Models and Explanations Feedback and Adaptation Errors and Trust Calibration
Outcomes
What you'll learn
- Understand AI capabilities — Learn when AI adds unique value versus when simpler solutions work better for users.
- Design trustworthy AI systems — Master techniques for building appropriate user trust through transparency and explanations.
- Create adaptive experiences — Design for systems that learn and change over time while maintaining user understanding.
- Handle AI uncertainty gracefully — Develop strategies for managing errors, low confidence, and edge cases in AI products.
Before you begin