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Human-AI Product Design And Learning Experience

Design AI tools that people can understand, use and correct.

About This Course

Introduction

A capable model can sit inside an app that leaves people confused. What information did it use? Did it send the message, or just draft it? What can I do if the answer is wrong? Human-AI product design connects the system's behavior to the person's task, understanding, and control.

We will explore needs, workflows, mental models, affordances, feedback, trust, review, accessibility, interruption, and learning design. The same ideas apply to this course: clear scope, manageable sessions, visible progress, a place to restart, and practice that explains the answer. People can benefit from these choices without needing a diagnostic label.

Review task evaluation in Post-Training, Adaptation, And Evaluations and decision authority in Decisions, Forecasting, Optimization, And Causality. Your project is a product-and-learning blueprint tested with users. Start with tasks and interactions, then examine failures and control, and finish with learning and measurement. Use an interview-preparation assistant and a beginner learning AI as your examples.

Planned Lessons

  1. Begin With The Person's TaskStart designing an AI tool with what a person needs to get done.
  2. Mental Models And Expectation SettingHelp people understand what an AI tool is likely to do and where it may struggle.
  3. Affordances, Signifiers, And Visible StateMake the next action and the current state of an AI tool easy to see.
  4. Choose The Interaction Form That Fits The WorkChoose a way of interacting that fits the person’s work.
  5. Onboarding And The First Useful SuccessHelp someone reach a useful first result without unnecessary setup.
  6. Trust Should Follow EvidenceHelp people base their trust in AI on evidence of how it performs.
  7. Review Effort And The Human-In-The-Loop PromiseCount the time and effort a person needs to check an AI result.
  8. Errors, Explanations, And RecoveryGive people a clear way to understand a problem and put it right.
  9. Accessibility And Cognitive EffortMake an AI tool easier to see, navigate, remember and understand.
  10. Feedback That Supports Correction And LearningGive feedback that helps a person correct a mistake or learn from an attempt.
  11. Personalization With ControlAdapt the experience while letting the person see and change their preferences.
  12. Agency, Actions, And Meaningful ControlDecide which AI actions people must approve and how they can stop or correct them.
  13. Designing Activities That Test LearningCreate learning activities that show whether someone can use an idea.
  14. Short Sessions, Interruption, And Read AloudSupport short study sessions, interruptions and listening instead of reading.
  15. Usability Testing With Concrete TasksWatch people use a tool for real tasks to find where the design causes problems.
  16. Product And Learning OutcomesMeasure what people achieve and learn alongside how often they use the product.