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AI for Designers

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How This Course Will Help Your Career

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    Do you feel intimidated or overwhelmed by the speed at which AI is advancing and impacting our work as designers? Join me on a journey into the age of A.I. We’ll explore how A.I. is shaping the way we work and the future of product and UX design. You'll understand why A.I. is your partner; an opportunity to work better and smarter; what designing for A.I. products entails; how we can augment our design process with the help of A.I. and much more.

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    With the right framework, thinking and systems, A.I. can be a force for good. And as designers, we have an important role in making sure that this will happen. Learn how to advocate for good A.I. practices and become a voice for good by joining me on this course.

Empower yourself with cutting-edge skills that will enable you to both seamlessly incorporate artificial intelligence (AI) tools into your design process and learn the basics of how to design for AI.

In this comprehensive course, you will explore AI's impact on design and how to make the most of AI and your human skills to future-proof your career. Enhance your workflow, tackle real-world challenges, and position yourself at the forefront of design innovation.

Key Learnings:

  • Explore AI Impact: Understand the transformative impact of AI on the design industry and anticipate future trends.

  • Optimize Your Workflow: Discover how AI tools can make your design process much more efficient, including UX research, prototyping, and evaluation.

  • Integrate AI Tools: Introduce AI into almost every step of your design process to make it more streamlined than ever before.

  • Design for AI: Master the basics of designing for artificial intelligence and understand its nuances, unique challenges and opportunities.

  • Help to Tackle Real-World Challenges: Address real-world design challenges using AI-driven solutions and ethical considerations.

  • Stay Innovative: Take your place at the cutting edge of design innovation by embracing AI technologies and methodologies.

In an era where technology is rapidly reshaping the way we interact with the world, understanding the intricacies of AI is not just a skill, but a necessity for designers. The AI for Designers course delves into the heart of this game-changing field, empowering you to navigate the complexities of designing in the age of AI. Why is this knowledge vital? AI is not just a tool; it's a paradigm shift, revolutionizing the design landscape. As a designer, make sure that you not only keep pace with the ever-evolving tech landscape but also lead the way in creating user experiences that are intuitive, intelligent, and ethical.

AI for Designers is taught by Ioana Teleanu, a seasoned AI Product Designer and Design Educator who has established a community of over 250,000 UX enthusiasts through her social channel UX Goodies. She imparts her extensive expertise to this course from her experience at renowned companies like UiPath and ING Bank, and now works on pioneering AI projects at Miro.

In this course, you’ll explore how to work with AI in harmony and incorporate it into your design process to elevate your career to new heights. Welcome to a course that doesn’t just teach design; it shapes the future of design innovation.

In lesson 1, you’ll explore AI's significance, understand key terms like Machine Learning, Deep Learning, and Generative AI, discover AI's impact on design, and master the art of creating effective text prompts for design.

In lesson 2, you’ll learn how to enhance your design workflow using AI tools for UX research, including market analysis, persona interviews, and data processing. You’ll dive into problem-solving with AI, mastering problem definition and production ideation.

In lesson 3, you’ll discover how to incorporate AI tools for prototyping, wireframing, visual design, and UX writing into your design process. You’ll learn how AI can assist to evaluate your designs and automate tasks, and ensure your product is launch-ready.

In lesson 4, you’ll explore the designer's role in AI-driven solutions, how to address challenges, analyze concerns, and deliver ethical solutions for real-world design applications.

Throughout the course, you'll receive practical tips for real-life projects. In the Build Your Portfolio exercises, you’ll practice how to integrate AI tools into your workflow and design for AI products, enabling you to create a compelling portfolio case study to attract potential employers or collaborators.

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Our clients: IBM, HP, Adobe, GE, Accenture, Allianz, Phillips, Deezer, Capgemin, Mcafee, SAP, Telenor, Cigna, British Parliament, State of New York

This course addresses a broad spectrum of professionals and is a practical handbook for anyone seeking to advance their design skills, optimize their design process and stay ahead of the curve.

In particular, this course will benefit:

  • Designers looking to enrich their design process with AI tools and explore a new creative frontier.

  • Entrepreneurs involved in AI product development.

  • Business Stakeholders and Product Managers aiming to enhance their brand with AI products or services.

Anyone keen on getting into cutting-edge technology and embracing the challenges and opportunities of this innovative field.

Course Overview: What You'll Master

  • Each week, one lesson becomes available.
  • There's no time limit to finish a course. Lessons have no deadlines.
  • Estimated learning time: 13 hours 27 mins spread over 7 weeks .

Lesson 0: Welcome and Introduction

Available once you start the course. Estimated time to complete: 1 hour 46 mins.

Lesson 1: Design in the Age of AI

Available once you start the course. Estimated time to complete: 2 hours 43 mins.

Lesson 2: How to Use AI tools for Research and Ideation

Available anytime after May 04, 2025. Estimated time to complete: 2 hours 52 mins.

Lesson 3: How to Use AI tools for Prototyping and Testing

Available anytime after May 11, 2025. Estimated time to complete: 2 hours 26 mins.

Lesson 4: How to Design the Future: Essential Factors for AI-Powered Products

Available anytime after May 18, 2025. Estimated time to complete: 3 hours 41 mins.

Lesson 5: Course Certificate, Final Networking, and Course Wrap-up

Available anytime after May 25, 2025.

How Others Have Benefited

Amber Stechyshyn

Amber Stechyshyn, Canada

“There are lots of resources to explore and learn from—I have a bunch of new sites to view and test!”

Nischal Saugh

Nischal Saugh, South Africa

“Amazing course! This has given me a different perspective on AI and how I can use it as a collaborator and functionality for my customers.”

Lora Andresen

Lora Andresen, United States

“The course covered a great deal of pertinent material on the topic and I appreciated the balance between the "how to" materials and the trickier ethical/responsibilities materials.”

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    Lessons are self-paced so you'll never be late for class or miss a deadline.

  2. Get a Course Certificate

    Your answers are graded by experts, not machines. Get an industry-recognized Course Certificate to prove your skills.

  3. Advance your career

    Use your new skills in your existing job or to get a new job in UX design. Get help from our community.

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AI for Designers
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AI for Designers

1.2 - Why Is AI so Important and How Is It Changing the World?

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    The information volume about AI and design is already overwhelming right now. So, to help me break my own learning into logical categories, I'm always thinking about an intersection of AI and design into two main frames: *designing with AI* and  *designing for AI*. When I talk about designing for AI, what I'm talking about is a whole new world  of design challenges and opportunities

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    that we have to navigate when building products in the age of AI. AI interactions in our products require a new way of thinking. And in an article published by Jakob Nielsen, he announces AI as the first new UI paradigm in 60 years. What does this mean? In conventional systems based on command interactions, the user issues commands to the computer one at a time until they reach the desired result, if – hopefully – the system is user-friendly enough to allow people to figure out

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    what commands to issue at each step. The computer listens to our commands  and executes them, hopefully as instructed. In the world of the new AI systems, the user doesn't tell the computer what to do, but instead they tell it the *outcome they hope to achieve*. So, Jakob Nielsen calls this *intent-based outcome specification* and argues that, compared to traditional command-based interactions, this paradigm completely reverses the locus of control.

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    Where we once ran the machine,  now we let the machine run itself. An example would be creating images. Let's say we want to create a digital illustration of a mountain. In a traditional UI system, we'd probably open Photoshop and start adding one instruction on top of the other, draw a triangle, add fill, round corners, add texture, and so on. With AI systems, we go to image generators like Midjourney or DALL-e and instruct it on what kind of image we want to get through prompts: "Draw me an image of a mountain at sunset." And then the computer does the work for you.

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    So, if you think about it, we'll be designing for a new realm of experiences, completely new user expectations, mental models, and fundamentally different interactions. Many of the traditional products we use are adding AI capabilities. And this is a trend that we will see expand through our product companies. So, it's pretty likely that in the future most of us will have had some sort of experience designing for AI interactions in our roles.

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    And then, for the second framing, *designing with AI*. I want to start with an idea that has been quite viral on social platforms for the past year. AI won't replace you. A person using AI will. What this means is that AI by itself doesn't hold a power to replace us, mostly because it's infant technology and it holds multiple limitations, some of which are not resolvable in the foreseeable future. For one, it can't figure out what problems to solve yet. AI has trouble understanding context, and because we're still in the age of narrow AI

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    where AI tools can only do one type of task, it's very hard to solve complex problems with AI alone. It's true that research is being done in the space of collaborative intelligence where under the guidance of a governing AI different AI models work together to tackle more sophisticated tasks and solve complex problems. But right now AI is mostly a one-trick pony, so it can't possibly understand complex systems like a person's context. Their psychology, environment, background, needs, goals,

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    aspirations, relationships interpret all the connections and understand how they might make this person's life better. Only humans can understand humans deeply enough to really address the problems they struggle with. Then, design solutions require *multi-disciplinary efforts*. Any design solution requires systems thinking making connections between multiple fields and sciences. Understanding interface design, human psychology, information architecture,

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    visual principles, accessibility design, anthropology and sociology, business strategy, content strategy,  user research, and so on. AI systems can't handle this level of complexity in grasping landscapes and putting different perspectives and disciplines together. *AI also lacks empathy and a good  understanding of human psychology*. Also, its ability for creativity and imagination is subject to debate. For a long time, I've hated the term "empathy".

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    I felt it was overused to the point it lost  meaning, it became overly diluted. But I believe it's making a spectacular comeback in the age of AI because I personally can't think of a better word that captures what will essentially make  us different from computers forever. We have the capacity of imagining and attempting to even feel what the other person feels. Even though computers might mimic a conversational apparent empathy or compassion, computers will never really feel, regardless of how well they'll be able to emulate that.

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    Also, human creativity and imagination are quite special, even magical I would say. Even though AI can successfully simulate human creativity by putting together existing elements to create something new, in a similar fashion in which people do that, that human special spark comes from imagination: To be able to think of something new. And if you think about it, just looking at AI-generated art will sort of tell you it's been generated by AI.

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    It's pretty stereotypical; you get the feeling it all looks the same, like something we've seen before. AI still needs a lot of guidance, handholding, and gets lost outside its context. AI can easily go wrong and hallucinate. This is a real technical AI industry term. We've seen some funny and some very worrying examples, but the gist of it is that AI needs us to hold its metaphorical hand. It doesn't perform very well by itself. But even with all these limitations, designers who understand that AI is an  opportunity for an exoskeleton

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    that enhances and augments their natural capabilities will increase their chances of remaining competitive in a market where most of the work will be produced by human-AI collaboration. AI can already support us with making better decisions faster, reducing our cognitive load from having to process large volumes of data, spend time on more meaningful and creative work, kickstart our work projects, artifacts  faster, increase the accuracy of our efforts, and so much more.

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    In the end, there's probably not going to be much escape from AI changing the way we work; so, you might as well prepare to become the person that uses AI to remain competitive.

AI for Designers

1.7 - How to Craft Effective Text Prompts for Design

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    Creating effective prompts for AI systems requires understanding both the capabilities and limitations of the AI and the context in which it will be used. Here are some strategies to optimize your text prompts. *Specify the format*. If you need the AI's response in a specific format, include that in your prompt. For example,

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    "Write a 3-paragraph summary of the following article." *Provide context*, especially for more complex tasks. Providing context can help guide the AI towards a more accurate response. You could give the AI a brief overview of previous related exchanges or a description of the current situation; or, even better, *provide examples*. You can include examples in the prompt that show the model what getting it right looks like. What we call *few-shot prompts*, *one-shot prompt*.

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    The model attempts to identify patterns and relationships from the examples and apply them to form a response. *Iterative refinement* – refine your prompts based on the AI's responses. If the AI is not producing the desired result, consider adjusting the specificity, tone or format of your prompts. I've personally learned that it typically takes me four or six iterations of a prompt before I can achieve the best results for a specific output I hope to get when interacting with AI.

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    And remember, creating effective prompts often involves a process of trial and error. Continually testing and refining your prompts will lead to better results over time.

AI for Designers

2.2 - How to Supercharge Your Design Workflow with AI

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    Before showing you an end to an AI-powered design process. That still happens under human guidance, which is the key point in this conversation. Let's go quickly over how AI can help us. So for ones that can decrease cognitive load, aid decision making by processing large volumes of data, can help us automate repetitive tasks such as formatting images or resizing text.

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    It can help us by providing more insights into human behavior and usage patterns. Also, with creating prototypes or mockups, all kinds of visual assets, it can spot usability issues and so on. AI can help us at every step of the design process. Covering our blind spots, augmenting our thinking, making us more efficient if we use it correctly. Here's how an end to end design process augmented by AI may look like. But the key to reading this schema are how I prefer to look at it:

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    there's always a person orchestrating this. The future in which AI could generate a cohesive, coherent, reliable and relevant design process end to end is a very distant future for now, and there's an impetuous need for someone governing over this process, applying critical thinking and showing intentionality at every stage of the design process. Also, because AI shows multiple limitations

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    on each of the steps shown in the schema. Nielsen Norman Group published an article unpacking the limits that currently surround the use of AI in research. To understand the context of their analysis. You need to know that there are currently two type of AI powered research tools we currently see on the market: insight generators and collaborators. AI Insight generators. These tools summarize user research sessions based only on the transcripts.

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    Since they don't accept any kind of additional information (context, past research, background information about the product and users and so on), they can be highly problematic in how they generate and present those summaries. While there are some workarounds like uploading background information as session notes to be added to the analysis, it's not the right framing for the source and it's not going to reflect correctly in the analysis and generation. Humans would be much better at this. The scoping and systems

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    thinking required to understand the interpretation landscape AI collaborators. These work similar to insight generators, but they're slightly better because they accept some contextual information provided by the researcher. For instance, the researcher might show to the AI some human generated interpretation to train it. The tool can then recommend tags for the thematic analysis of the data in addition to session transcripts, collaborators can also analyze researchers notes

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    and then create themes and insights based on input from multiple sources. But even though they appear to be a bit better, they're still significantly limited and pose a lot of problems, if not used with the right mindset and caution. The limitations they've identified and expand on in detail are: most AI tools can't process visual input, and the biggest problem with that is no human or AI tool can analyze usability testing sessions by the transcript alone.

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    Usability testing is a method that inherently relies on observing how the user is interacting with the product. Participants often think-aloud describing what they're doing and thinking. Their words do provide valuable information. However, you should never analyze usability tests based only on what participants say. Transcript-only analysis misses important context in user tests because participants don't verbalize all their actions don't describe every element in the product. Not always have a clear understanding or mental model of the product.

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    So for now, Nielsen Norman's group recommendation is do not trust AI tools that claim to be able to analyze usability testing sessions by transcripts alone. Future tools able to process video visuals will be much more useful for this method. Another problem is the limited understanding of the context. This remains a major problem. AI insight generators don't yet accept the study goals or research question insights or tags from previous

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    rounds of research, background information about a product or the user groups, contextual information about each participant, new user versus existing user, the list of tasks or interview questions. There is also a problem with the lack of citation and validation, which raises multiple concerns and problems. The tools aren't able to differentiate between the researchers notes and the actual session transcript. A major ethical concern here. We must always clearly separate our own interpretation or assumptions from what the participants said or did.

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    Another problem with the lack of citation is that it makes verifying accuracy very difficult. AI systems can sometimes produce information that sounds very plausible, but is actually incorrect. Unstable performance and usability issues are another problem. None of the tools they tested had solid usability or performance. They reported outages, errors and unstable performance in general. And then there's the problem of bias. According to Reva Schwartz and her colleagues, AI systems and applications can involve biases

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    at three levels: systematic, statistical and computational and human biases. AI must be trained on data which can introduce systematic such as historical and institutional and statistical biases, like a dataset sampling that is unrepresentative enough. When people are using a AI-powered results in decision making they can bring in human biases like anchoring bias. So bias can creep into research efforts on multiple levels,

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    and these tools don't yet have the mechanisms in place to prevent that. I wanted to discuss the limitations reported in the article in detail, because I believe we can easily extrapolate and expand them beyond just research tools. Most of these problems will be observable on other types of AI companions in the design process. Biases in image generation, limitations in being offered context, other kinds of input limitations, not accepting files or images,

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    output vagueness, generic results, and so on. So I think that this is a necessary frame to keep in mind when interacting and designing with the help of AI. Tools are not very reliable yet and accurate. So take everything they produce with a grain of salt and apply critical thinking at all times.

AI for Designers

3.9 - Design Freedom: Automate Repetitive Tasks in the Design Process

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    The stages of the UX process are  not singular in offering opportunities for enhancing our ways of working. We can also look into automating side process things such as productivity or collaboration. I would personally start with the parts that we're naturally not very good at or the ones we don't enjoy doing. Who  wouldn't be excited to get rid of some of the menial tasks?

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    So, here are some ideas for using AI  in our design work: Creating icons with Midjourney; generating presentations with tools such as Tome or Beautiful.AI; stock imagery with Midjourney, DALL-E, Stable Diffusion; color palettes with Chroma; writing documentation with Notion AI, writing with Grammarly, and many other options. The landscape of AI tools is rapidly changing. So many new capabilities pop up every week. The best best way to explore ways of optimizing your work is to go to

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    platforms like Futurepedia and see what's new, play around with the tools, learn and experiment on your own. I choose to set aside 2-3 hours  in my calendar every week, AI playground time, where I simply experiment as much as I can with whatever I stumble upon. It may be something worth trying.

AI for Designers

4.3 - AI Challenges and How You Can Overcome Them: How to Design for Trust

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    AI can hallucinate and its behavior cannot be predicted most of the time. We can't anticipate what the user will be presented with as we can in a traditional interaction system where we design the interactions and the messaging step by step conventional user experiences are controllable. We understand and decide what happens next.

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    With AI, we have to accommodate surprises and equally important, communicate the risk for upfront setting the right expectations about performance, indicating whether the product learns over time, clarifying that mistakes will happen and that user input will teach the product to perform better and so on. Don't omit this transparent initial communication as AI systems operate with uncertainty, and if your users expect deterministic

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    behavior from a probabilistic system, their experience will be degraded. Another major problem is trust and transparency. People have a hard time trusting objects that feel magic. We can't trust what we can't understand. AI systems are not transparent to us for multiple reasons. For once, most of us lack the technical knowledge to actually understand what goes on under the hood. Second, and more importantly, many times with generative AI,

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    we have no idea where the information is coming from. Tools like GPT and Bard have the power of constructing answers that feel legitimate and sound very pertinent, but can be entirely made up and inaccurate. AI doesn't tell us how they've constructed an answer which would help. And maybe as designers, we should push for this transparency in generative AI, exposing a high level thinking process that gives us the information sources

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    and general reasoning that's behind an answer. Another reason that adds to the mistrust is how AI is communicated in the media. A Google algorithm that classifies people of color as gorillas, a Microsoft chatbot that decides to become a white supremacist in less than a day, a Tesla car operating in autopilot mode that resulted in a fatal accident. We've seen these isolated but terrible experiences. Sometimes the AI systems are described as black boxes,

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    so maybe the solution is opening them up. Companies such as Google, Airbnb and Twitter already released transparency reports about government requests and surveillance disclosures. A similar practice for AI Systems could help people have a better understanding of how algorithmic decisions are made. The last problem I want to mention is ownership and intellectual property. This, for me is a fascinating, a very important debate.

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    I want to start by saying that with so much AI generated art, what we'll all witness is a significant shift in what we value as a society. And to illustrate this argument, I want to give the example of Mona Lisa. Borrowing this from Mark Rolston. If you think about it, AI could basically recreate a one on one replica of Mona Lisa without any identifiable differences. Could actually make it better, more symmetrical, more technically impressive, and so on.

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    But it won't be Mona Lisa. If we know and in the future, AI art will probably be labeled as per regulations that are not yet in place but are needed. Adobe is already adding those labels. More will follow If we know that it has been generated by AI and we look at it, we probably won't feel too much. But if we're in front of Mona Lisa at the Louvre, we know this was made by da Vinci 500 years ago.

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    We can see the fascination of the people around us, their excitement. We ourselves can experiment our own version of interpreting and looking at it. We're humbled by being in front of this work of art that has been worshiped by humanity for hundreds of years. It has the potential of being an almost religious, fundamentally human and very touching experience, which is very unlikely to be experienced towards AI art. We will value what is human made.

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    We will know that something was created from a person's experience, suffering, imagination, hope, scarcity is not what will make art valuable. Its creator is. And then there's another layer to this debate. If AI creates art based on everything it knows from the work of other artists is that such a different expression of creativity from that of a person who has been in art school, studied Picasso, Matisse, Mondrian.

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    And then their style is influenced by the art history and the works they studied. Something to think about. I'm not saying theft is acceptable, which takes us back to the need for a more transparent, cited, source exposing system for generative AI. But I want to give a different angle on how art is built.

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