GenAI Fundamentals for UX Designers + Researchers
GenAI Fundamentals for UX Designers + Researchers
Learn the key concepts and components to harness GenAI for UX design — and lead AI product innovation efforts.
Product Brand: Udemy
4.8
Udemy Coupon Code for GenAI Fundamentals for UX Designers + Researchers Course. Learn the key concepts and components to harness GenAI for UX design — and lead AI product innovation efforts.
Created by Joe Natoli | 3 hours on-demand video course | 1 downloadable resource
GenAI Fundamentals for UX Designers Course Overview
Protect and grow your career: Learn to design GenAI-enabled products. The UX and Product design landscape is evolving—and AI is leading the charge. If you’re a designer, UXer, or product developer, you can’t afford to be left behind. Generative AI is not just a buzzword; it’s becoming the backbone of the next generation of user experiences. And without a deep understanding of how to design AI-powered products, your skill set risks becoming obsolete.
I’ve included a massive GenAI UX workbook you can use on real-world projects. You can download and use this 164-page companion workbook to use both as practice and to help you through the process of designing for GenAI in your daily work as well. Use the guidance and 89 exercises across 22 mission-critical topics in GenAI design for any real-world GenAI projects that come your way — the exercises here are designed to help you through the process, from concept to execution.
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What you’ll learn
- Learn core principles of responsible GenAI design that ensure our products serve all users equally + equitably.
- Success factors unique to Machine Learning (ML) products that UX + Product Designers and their teams should adopt
- Guidance on research to determine what kinds of problems are best solved by AI, and where human control should remain central.
- Determining when AI features are appropriate for users — and when they aren’t
- Identifying when Automation (AI does the task for users) or Augmentation (help them do it better) is more appropriate.
- Designing the reward function and appropriately considering the balance between false positives and false negatives.
- Essential factors to consider when evaluating the reward function.
- How to weigh necessary, unavoidable tradeoffs between precision and recall, which is key to shaping an AI user experience.
- Designing for fairness and inclusion, from objectives to datasets to guidance on bias testing.
- Designing for generative variability: how do we present multiple, varied outputs to users — and how do we guide them in selecting the best one?
- Designing for multiple outputs: how do we help users filter and highlight differences between outputs?
- Designing for imperfections: how do we empower users to manage + mitigate imperfections and designing with contextual sensitivity?
- Designing for confidence: how do we design confidence scoring to properly evaluate output quality and increase user trust?
- Rules and examples for applying confidence scores.
- Designing for co-creation: how to we design co-creation processes where both the user and the AI can make adjustments?
- Designing for generic controls: using “temperature” to control the number of outputs and the degree of variability in those outputs.
- Designing for domain-specific controls, such as encoder-decoder models, semantic sliders and prompt engineering.
- Designing for prompt engineering: enabling and guiding users to effectively use multiple types of conversational prompts.
- Designing for exploration: incorporating flexibility, feedback, transparency and error handling/expectation management.
- Designing for choice, feedback, transparency + safety: centering users as active, empowered participants in the creation process.
- Designing for mental models: orienting users to generative variability, teaching effective use and teaching the AI about the user.
- Designing for explanation, understanding + trust: providing clear rationales for outputs, using friction to curb over-reliance and showing imperfections.
- Designing against harm: understanding the critical ways irresponsible AI design can harm people.
- Designing against hazardous outputs: discrimination, exclusion, toxicity, misinformation, deep fakes, IP theft and more.
- Mitigating harm with a Value-Sensitive Design (VSD) process, integrated with an Agile or Lean development process.
Who this course is for:
- New UX and Product Designers: If you’re starting out and want to make your career trajectory more stable and secure, this course will give you the foundation to be an asset to your employer from day one.
- Experienced UXers, Product Designers and Product Developers: If you’re already working in the industry and want to stay competitive and lead the next wave of innovation, this course will teach you how to begin integrating AI product design into your current design skill set — and to have informed, productive conversations with software engineers and data scientists on GenAI projects.
- UX Researchers, UX Writers, Product Managers and anyone wondering what they need to know in order to play a role in designing GenAI-driven products.
