Course Curriculum

5 Sections
00

Introduction and Essential AI Terms

3 Lessons 25 min
  • Course Overview & Downloads 5 min
  • Artificial Intelligence and Machine Learning: How Do They Relate? 8 min
  • What is Generative AI? 3 min

Practice Activities

  • AI Classification Challenge 9 min

Resources Provided

  • 0.1_CheatSheet_Understanding-LLMs (PDF)
01

How Does AI Learn?

2 Lessons 49 min
  • What is Training Data? 11 min
  • Why Good Data Matters 7 min

Practice Activities

  • Questions for Your Data Scientist (Instructions) 18 min
  • Questions for Your Data Scientist (Walkthrough) 13 min
02

Large Language Models (LLMs)

3 Lessons 40 min
  • The Core Task of LLMs: Word Prediction 5 min
  • Limitations of LLMs 6 min
  • LLM Hallucinations 4 min

Practice Activities

  • How Well Can You Explain It? (Instructions) 21 min
  • How Well Can You Explain It? (Walkthrough) 4 min
03

When to Use AI: How to Make Decisions

2 Lessons 48 min
  • Where AI Helps 16 min
  • Where AI Can Hurt 8 min

Practice Activities

  • Where Can AI Help? (Instructions) 17 min
  • Where Can AI Help? (Walkthrough) 7 min
04

Explaining AI

3 Lessons 45 min
  • What do Your Stakeholders Care About? 13 min
  • Gather Your Allies 6 min
  • Course Conclusion 2 min

Practice Activities

  • AI Stakeholders (Instructions) 19 min
  • AI Stakeholders (Walkthrough) 5 min

Learning Outcomes

  • Explain how Large Language Models work to non-technical stakeholders.
  • Explain the main limitations of Large Language Model-based AI.
  • Justify using or avoiding AI in everyday work tasks and design solutions.

Course Preview

 to watch NN/g videos

Tools Used in This Course

To complete activities in this course, you will need access to a powerful general AI tool such as Microsoft’s Copilot, Google’s Gemini, OpenAI’s ChatGPT, or Anthropic’s Claude.

While not required, we recommend that you access these tools via a paid account for better performance.

All other resources can be accessed through a standard PDF reader.

Turn Problems Into Solutions

Ask the right questions when building AI

  • Guide your team to choose the right AI tool based on performance, cost, and product scope
  • Collaborate with data scientists to identify potential sources of bias in training steps and training data
  • Identify and address stakeholder concerns and priorities around AI

Explain AI concepts to colleagues and stakeholders

  • Differentiate between Machine Learning, AI, genAI, and LLMs
  • Communicate how LLM training steps can affect a final AI product
  • Accurately convey the costs and risks of AI development to stakeholders

Avoid creating AI workslop and losing fundamental UX skills

  • Minimize offloading excessive critical thinking to AI
  • Reduce the amount of necessary AI output checking
  • Leverage AI to build skills rather than replace them

What People Are Saying

  • I found the course highly relevant and topical for my work. I work at a consultancy as a designer, and I’m continuously asked to contribute to genAI projects. The course has helped me revisit core concepts, introduced new and useful ideas, and even provided practical frameworks for application.

    Ana Khachatrian
    Sr. Product Designer, Beta Participant

  • What I especially liked was that I could immediately apply the insights to real-world projects in my day-to-day work, nothing felt abstract or disconnected from practice. Much of the content was new to me, yet it was presented in a very clear and understandable way.

    Yurii Husynskyi
    Product Designer, Beta Participant

  • The course components were well structured and professionally delivered. The videos were clear and engaging, with excellent visual examples. The downloadable resources and activities reinforced the learning objectives effectively, and the final assessment thoroughly tested my understanding of the material.

    Khaled Aldarami
    Senior Researcher & Innovation Strategist, Beta Participant

  • This course was well structured and very valuable for understanding the what, why, and how of LLMs. It’s easy to assume that AI can do everything, but the course showed that its real value comes from using it thoughtfully and intentionally.

    Sonali Agrawal
    Head of Product Design, Beta Participant

Frequently Asked Questions

How long will I have access to this course?
Once purchased, your access will never expire. This ensures you can learn at your own pace and return to review content as often as you'd like, whenever you need.
Will I get personalized instructor feedback on course activities?
No, personalized feedback is not provided for activities completed during the course. Most courses provide answer keys to activities that help learners reflect on their progress and understanding of course materials and examples.
What content can I download?
You can download a variety of resources provided with each course, including templates, how-to guides, cheat sheets, posters, reading lists, and more. Video lessons provided in all courses will not be available for download.
Can I access this course offline or on a mobile device?
Offline access isn't available yet, although we plan to add it. Most courses offer downloadable resources for offline reference.

For the best experience, mobile devices are not recommended.
Is this course eligible for the NNGroup UX Certification Program?
Unfortunately, self-paced courses do not count toward UX Certification. However, learners who complete the final exam will receive a “Recognition of Completion” to demonstrate their achievement.

See our Live Online courses for options that count toward UX Certification.
What can I share with my employer to show I’ve completed the training?
Upon completing a course’s final assessment, you will receive a “Recognition of Completion” showcasing your dedication to learning the topic. This document can be shared with your employer or professional network to demonstrate your new skills and commitment to professional development.