Artificial Intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, understanding natural language, and adapting to new information.
AI applications range from virtual assistants and image recognition to complex tasks such as autonomous vehicles and medical diagnosis. They contribute to sustainable design by optimizing resource use and reducing waste.
In this video, AI Product Designer Ioana Teleanu talks about how AI is changing the world.
AI systems use algorithms and computational models to analyze vast datasets, identify patterns, and make decisions. Machine learning, a subset of AI, enables systems to improve performance over time by learning from experience without explicit programming, which can support a circular economy by enhancing resource efficiency and recycling processes. Deep learning, a specialized subset of machine learning, centers around deep neural networks with multiple layers, which mimics the human brain's complexity. These networks autonomously extract intricate patterns from extensive datasets, enabling advanced capabilities like image recognition and natural language processing, and can be applied in incremental modular design to iteratively improve products and systems.
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Artificial Intelligence encompasses a spectrum of capabilities, from specialized task-oriented systems to intelligence that mirrors human cognitive functions. At the core of this distinction lies the difference between Narrow AI, also known as Weak AI, and General AI, also known as Strong AI or Artificial General Intelligence (AGI).
Narrow AI refers to systems tailored for specific, well-defined tasks within a limited scope. Examples of narrow AI models are common in our daily lives, from voice recognition tools like Siri or Alexa to recommendation algorithms powering platforms like Netflix and Spotify. Chatbots assisting with customer service on websites and specialized image recognition software in facial recognition or medical imaging analysis are also instances of narrow AI. Its defining characteristic is its lack of capacity to generalize knowledge beyond its designated domain.
On the other end of the spectrum is General AI, an advanced form capable of comprehending, learning, and applying knowledge across various tasks—mimicking the breadth of human intelligence. Unlike narrow AI, AGI can reason, problem-solve, adapt, and exhibit self-awareness. The ultimate goal of AGI is to perform any intellectual task that humans can, seamlessly transfer knowledge between domains, and autonomously improve over time.
While narrow AI excels in specific functions, AGI is the pinnacle of AI development. Currently, however, most AI systems are narrow, designed for specialized tasks and lacking the broad adaptability of AGI. Achieving AGI remains the significant and ambitious objective of AI research and development.
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On the spectrum of AI, generative AI is positioned between narrow and general AI. It’s a category of artificial intelligence that focuses on creating new content, data, or artifacts rather than performing specific predefined tasks, embodying the principles of humanity-centered design to ensure ethical and societal benefits. It involves machines that can produce outputs, such as images, text, or other forms of content, that weren't explicitly programmed into them. Generative AI often employs deep learning and neural networks to learn patterns from large datasets to generate novel outputs. Outputs are created in response to AI prompts. Effective prompts, or prompt engineering, are an essential part of human-ai interaction.
ChatGPT, a generative language model by OpenAI, was released in 2022. Within five days, over a million people had signed up for it. Unlike traditional programs with fixed responses, ChatGPT can dynamically generate answers based on the patterns it learned from vast amounts of text data. This ability makes it versatile—you can ask it questions, request information, or even use it for creative writing. This type of AI is valuable for various tasks, from aiding in research to helping with creative projects.
DALL-E, another application from open AI, generates images. Similar to ChatGPT, it creates a unique output from text inputs or prompts. For instance, you can ask DALL-E to generate an image of a "giant rubber duck" or a "surreal cityscape with floating buildings," and it will produce an original image matching that description. This kind of AI is part of the broader category of generative models designed to create new content. DALL-E showcases how AI can be used for artistic and creative endeavors, offering users a new way to generate visual content.
AI-generated art refers to artworks that are created with the assistance or direct involvement of artificial intelligence. In this process, artists or an individual collaborate with AI systems, which can include machine learning models and generative algorithms. These AI tools analyze vast datasets and learn patterns to generate new artistic outputs. AI-generated art spans various forms, including visual arts, music, literature, and more. The unique aspect of AI-generated art lies in the fusion of human creativity with the computational capabilities of AI, challenging traditional ideas of the arts and opening up new possibilities for artistic expression.
Unsupervised from Refik Anadol's Machine Hallucinations project, is a fascinating example of AI-generated art. It exemplifies the intersection of technology and creativity. Unsupervised, a product of deep learning algorithms processing vast datasets from the Museum of Modern Art (MoMA), generates abstract images guided by intricate patterns and associations within the museum's collection. This artwork is a testament to the capabilities of generative AI—its potential to create unique and unexpected outputs beyond explicit programming.
Learn more about AI-generated art, its challenges and opportunities in this video.
Transparency becomes a crucial concern as the origin of information and the decision-making process of these AI systems can be obscured. The potential for bias, privacy implications, and the need for explainability in AI-generated content underscore the intricate landscape that artists and technologists navigate.
In this video, UX design pioneer Don Norman, talks about how we can collaborate with AI—AGI is not a reality just yet so the AI apps we use need human input.
UX design pioneer Don Norman warns that these programs are not truly intelligent yet. They don't have wants, needs or a sense of self as humans do. Instead, they make decisions based on patterns in data too large for humans to process.
AI follows a complex set of logical rules called algorithms. Multiple algorithms connect in a way that mimics the human brain, called a neural network. This network can learn and improve its process over time. We call this "machine learning."
Artificial intelligence has already improved technologies like voice recognition and language translation. Even still, AI has shown even more potential and some surprising new applications.
For example, AI can create art and literature in the style of human authors and artists. Yet, they don't express emotions or create their own artistic style without human help.
This emerging technology has a variety of exciting and frightening uses. AI programs make it easy to pretend to be someone else or pass off AI content as your own. On top of that, the ethics of sentient AI will be a hot topic in various fields as the technology advances.
Text Generators
ChatGPT: This program can write new text by comparing itself to similar works on the subject.
Bard: A chatbot by Google used to create a more intelligent and conversational search algorithm.
Image Generators
Midjourney: An image generator that uses subject and style prompts to create new works of art.
Dalle-2: Similar to Midjourney but specializes in realistic images.
Video and Speech
Gen-1 Video editor: A video editor that shifts a video into a different style. For example, making a live-action video into an animation.
DeepFaceLab: One of many programs that make "deep fakes." Deep-fakes are videos that change faces and voices to impersonate other people. The most famous example is Jordan Peele’s Obama deep fake video from 2018.
Dragon Speech recognition: This program learns speech patterns to turn speech into text. It was the basis of most modern speech recognition software.
UX Design
Galileo AI: Entire user interfaces can be generated based on text prompts.
Genius: The AI design companion for Figma that fleshes out a full layout from a few design elements.
Interaction designers use AI technologies in a variety of ways. Artificial intelligence improves search algorithms for web searches, streaming services and other platforms. They can analyze terabytes of data to find patterns a human brain couldn't.
There is no doubt that AI will change how users interact with products and services. AI voice assistants and chatbots are examples of interfaces that adapt to user inputs in real-time. UX designers design the voice and the functions of voice assistants to appeal to users. Even though chatbots are text, they still need to make sense in the product's context of use. Like any interface, designers want to make a user experience that users trust and enjoy using.
“There’s a very simple formula, perceived trustworthiness plus perceived expertise will lead to perceived credibility.
Since AI is in service to human beings, I can't imagine a case where UX isn’t relevant…If you blow the UX design, it doesn't matter how good the AI is.”
-Dan Rosenberg, UX Professor at San Jose University.
The goal of artificial intelligence today is to be credible. They should be reliable tools and assistants for humans performing specific tasks. This credibility comes partly from a well-designed user experience and intuitive user interface.
The potential for AI to replace human workers is possible. But, it is more likely to be used to assist humans in making decisions. For example, AI could assist in usability tests or find patterns in user feedback or other user research tasks. AI has the potential to transform the essential tasks of a UX researcher.
“When it comes to [user] research, it is such a strategic discipline I can't think that we will ever automate it. If we are talking about general usability testing, that is going to be something where AI is going to play a big role. AI does something extremely well and that’s pattern recognition.”
-Greg Nudelman, Head of Design at LogicMonitor and Author on UX for AI
Many experts see the potential for AI to change human-computer interaction but also have doubts. AI systems can improve data analysis, assist translation, and help creatives bring ideas to life.
Yet, all this brings up deep ethical questions. Creatives of all types are forced to compete with AI, which can plagiarize their work in minutes. The question of who owns that AI content is also unclear.
Some communities have banned AI art entirely, even as the ability to tell them apart from human work diminishes. Even if AI does not fully replace humans, what will our economy or workplace look like if AI replaces daily tasks or even jobs?
In the future, “Strong AI” would learn, think, and generally function on the same level as humans. As fully sentient beings, there are moral questions of ownership and legal definitions of autonomy to grapple with.
Despite these challenges, tech companies are investing heavily in AI to explore the possibilities.
Discover how to design for AI and how you can incorporate AI tools into your design process in our course, AI for Designers.
For more on the role of AI and other technologies on design, take our course: Design for a Better World with Don Norman.
Norman, Donald A. Design for a Better World: Meaningful, Sustainable, Humanity Centered. Cambridge, MA, MA: The MIT Press, 2023.
Read more articles and essays by Don Norman on JND.org.
Watch our Master Class: AI-Powered UX Design: How to Elevate Your UX Career with Ioana Teleanu.
Watch our Master Class: How To Design for and With Artificial Intelligence with Dan Rosenberg.
Watch our Master Class: How To Design Experiences for AI with Greg Nudelman.
To learn more about the differences between AI, machine learning, deep learning and neural networks, read AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?.
Read more about AI and its various applications in the Investopedia article, Artificial Intelligence: What It Is and How It Is Used.
Everything About Usability Testing Through Al-Powered Software
Artificial Intelligence (AI) is the broad field of making machines perform tasks that normally require human intelligence. Machine learning (ML) is a subset of AI that allows systems to improve through data and experience without explicit programming. For example, a recommendation engine learns your preferences as you stream shows.
Deep learning is a subset of ML that uses multi-layered neural networks to analyze vast, complex datasets. This permits advanced capabilities such as speech recognition, image classification, and natural language processing (NLP). AI is the umbrella, ML is the approach to learn patterns, and deep learning is the advanced method that powers the most powerful tools today.
Explore what machine learning can do.
Neural networks are computational models which the human brain has inspired. They process information through interconnected nodes (“neurons”) that detect patterns across massive datasets. Each layer of the network extracts features: the first might detect edges in images, later layers recognize shapes, and deeper layers identify objects.
In design, neural networks fuel AI systems that perform image recognition, natural language understanding, and personalization. They enable tools like auto-tagging photos, suggesting layouts, or interpreting voice commands. For designers, neural networks open opportunities to create interfaces that feel intuitive, adaptive, and human-like. However, they also raise challenges around transparency, bias, and control. When designers understand how networks make predictions, they can ensure systems remain explainable, trustworthy, and aligned with user needs.
Get right into neural networks to understand how to leverage AI “brains” for the better.
The purpose of narrow AI, or “weak AI,” is to perform a specific task exceptionally well—for example, a chatbot answering customer queries. These tools do not “understand” context beyond their domain.
General AI, or “strong AI,” is hypothetical and aims to replicate human-level intelligence. It would reason, adapt, and apply knowledge across any domain, just as people can switch from solving math to writing poetry.
All AI in use is narrow for the moment, effective within constraints but unable to generalize. General AI remains a distant goal of research. The key is to recognize limits; current AI can supercharge experiences in targeted ways but still needs human guidance, creativity, and ethical judgment to stay relevant and safe.
Grab a greater grasp of general AI to explore fascinating points about where AI and design may head in the future.
Generative AI creates new content, such as text, images, video, or code, based on patterns learned from massive datasets. Unlike traditional programs with fixed outputs, generative models like ChatGPT or DALL-E produce dynamic, original results in response to prompts.
You can use generative AI to brainstorm ideas, prototype interfaces, generate imagery, or explore variations quickly. For example, DALL-E can produce unique illustrations for a concept, while ChatGPT can draft microcopy for a user interface. Artists also collaborate with generative AI to explore new forms of creative expression.
The power of generative AI lies in its ability to augment creativity, speeding exploration and iteration, while humans provide direction, context, and ethical oversight to ensure outcomes serve human goals.
Get right into generative AI for a wealth of insights into its applications and more.
Yes, AI can generate user interfaces and whole design systems, but quality depends on context and human input. Some tools can produce layouts or design components from text prompts. These systems speed up repetitive tasks and help non-designers prototype ideas.
However, AI still lacks a deep understanding of user psychology, accessibility, and brand nuance. It can assemble components and mimic existing design languages, but it cannot fully replace human judgment in creating meaningful, usable, and differentiated experiences. So, designers must refine AI outputs to ensure alignment with user needs, brand voice, and accessibility standards. Overall, AI acts as a co-pilot, automating production and exploration, while humans drive the strategy, empathy, and creativity side.
Discover insightful points about how to harness artificial intelligence in design in our article Why Is AI so Important and How Is It Changing the World?.
No, AI will not replace designers anytime soon, but it will change their work. For example, experts argue that UX research is too strategic and contextual to automate fully. What AI excels at is pattern recognition, data processing, and generating options, which complement human creativity, empathy, and critical thinking. Designers will rely on AI to handle repetitive tasks like generating prototypes, analyzing usability data, or drafting copy.
However, rest assured that designers remain essential in defining problems, ensuring accessibility, shaping ethics, and crafting meaningful experiences. AI cannot grasp human emotions, cultural nuances, or values as people can. Soon, designers who embrace AI as a partner will thrive, while those ignoring it risk losing relevance.
Get a greater grasp of what AI means for UX designers in our article How Can Designers Adapt to New Technologies? The Future of Technology in Design.
Stay relevant by combining human strengths with AI capabilities. Build up your expertise in strategy, ethics, storytelling, and psychology, areas AI cannot replicate. Learn how AI works, from machine learning basics to prompt engineering, so that you can direct tools effectively.
Develop skills in facilitating workshops, interpreting qualitative insights, and shaping brand identity. Master accessibility and inclusive design to help ensure AI-driven products serve diverse users.
Experiment with AI-powered design tools to accelerate workflows, but maintain a critical eye; AI suggestions need refinement. Lastly, position yourself as a bridge that translates between AI systems, stakeholders, and users. AI cannot “take over” as long as designers embrace adaptability, continuous learning, and ethical leadership in shaping the future of digital experiences.
We must make sure AI systems are trustworthy. So, begin with explainability; communicate how AI decisions are made in language that users understand. For example, show why a recommendation appears or how personalization works. Address bias by testing datasets for fairness and diversity. AI trained on skewed data can exclude or misrepresent groups.
Respect privacy, minimize data collection, anonymize records, and provide clear consent. Offer control, giving users ways to opt in, adjust, or override AI recommendations. Build accountability into interfaces by signaling when users interact with AI rather than humans.
Overall, AI interactions should be designed to respect autonomy, dignity, and trust. Ethical design ensures users see AI as a partner, not a hidden manipulator.
Find out vital points about respecting users in design and more in our article AI Challenges and How You Can Overcome Them: How to Design for Trust.
Prompt engineering means crafting inputs that guide generative AI toward useful outputs. Treat prompts like design briefs as a designer, be specific, contextual, and outcome focused. Instead of writing “make a button,” specify “generate a mobile-friendly call-to-action button, with accessible contrast, in a playful brand style.” Iterate; adjust prompts, compare results, and refine wording until you get there.
Use examples, constraints, and role assignment (“act as a UX researcher”) to shape responses. Document effective prompts for reuse in design systems. Test AI outputs against user needs and accessibility guidelines—never take them at face value.
Designers who master prompt engineering can transform AI from a gimmick into a powerful collaborator, accelerating exploration while keeping control over quality and direction.
Explore prompt engineering for a treasure trove of helpful insights into how to make the best use of it.
AI enables personalization by analyzing massive user datasets, such as user behavior, preferences, location, history, and tailoring experiences accordingly. Recommendation engines on Netflix or Spotify exemplify this; they predict content you will enjoy by recognizing patterns.
E-commerce platforms use AI to personalize product suggestions, while news apps surface stories that align with interests. In design, AI enhances personalization by adapting interfaces: changing layout, language, or tone based on user context. For example, a fitness app can adjust difficulty levels dynamically.
Done well, personalization increases engagement and loyalty, but designers must balance relevance with privacy: users want helpful customization, not intrusive surveillance. Ethical personalization means transparency, consent, and user control over data-driven adjustments.
Learn how to personalize via AI and more in our article How to Design with AI: 5 Insights to Supercharge Your Work.
Designers increasingly integrate AI into daily practice. For example, UX researchers use AI to analyze transcripts, detect sentiment, or cluster patterns in feedback. User interface (UI) designers employ tools to generate layouts or icons from text prompts.
UX writers use AI to draft microcopy, error messages, or multilingual variants. Design teams leverage AI chatbots for brainstorming, exploring edge cases, or generating user scenarios. And designers also use AI-powered testing to simulate user interactions and predict usability issues.
However, professionals always refine AI output, checking for accessibility, tone, and alignment with goals. The key is not to replace designers but to empower them with AI as a creative assistant that speeds iteration and broadens exploration.
Discover more about the essential skill of user research and understand why it is vital to help set the foundation for any UX design project or product.
Books
Huyen, C. (2022). Designing Machine Learning Systems. O′Reilly Media.
This book by Chip Huyen offers a comprehensive, practical guide to creating end-to-end machine learning systems. Targeted at intermediate to advanced practitioners, it covers 386 pages of strategies for moving ML projects from concept to production. Topics include system design principles, scalability, data pipelines, deployment, and monitoring—critical for delivering reliable, maintainable models. For UX designers working with AI-driven products, Huyen′s work is a valuable window into the technical and operational realities behind ML features. Understanding these constraints helps designers create user experiences that balance ambition with feasibility, ensuring that AI systems remain transparent, performant, and user-centric.
Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux.
In Artificial Intelligence: A Guide for Thinking Humans, Melanie Mitchell demystifies AI, tracing its history, breakthroughs, and persistent limitations. She blends accessible explanations with critical analysis, clarifying how AI systems actually work while confronting inflated claims. Mitchell addresses topics such as deep learning, computer vision, and natural language processing, as well as the philosophical and ethical questions AI raises. This book is especially valuable for UX designers and technologists, as it provides the conceptual grounding needed to engage with AI-driven design tools thoughtfully. It encourages informed skepticism and ethical awareness—key traits in designing responsible, user-centered technology.
Shane, J. (2019). You Look Like a Thing and I Love You: How AI works and Why It′s Making the World a Weirder Place. Little, Brown and Company.
In this humorous yet informative book, Janelle Shane uses real experiments with neural networks to explain how AI works, where it fails, and why those failures can be both funny and revealing. She breaks down complex AI concepts—such as training data, bias, and generalization—into approachable stories and quirky examples. For UX designers, the book offers valuable insight into AI′s strengths and limitations, helping them design interfaces and systems that account for machine learning quirks. Shane′s engaging style makes technical content accessible, fostering better communication between AI developers, designers, and end users.
Articles
Mika, A. (2025, July 22). AI in UX design: Principles, tools & examples. Ramotion. https://www.ramotion.com/blog/ai-in-ux-design/
Mika outlines where AI is making a real difference in UX—especially in user research, interface testing, and behavior analysis. The article includes visual examples and discusses how designers can responsibly apply AI without over-automating or compromising ethics. It′s a strong practical primer for UX designers integrating AI into daily workflows while maintaining empathy and transparency in their design decisions.
Nudelman, G. (2025, April 3). AI and UX design: The Iceberg UX model. UX Magazine. https://uxmag.com/articles/ai-and-ux-design-the-iceberg-ux-model
Nudelman introduces the “Iceberg UX Model” to explain how AI changes design priorities—shifting focus from visual UIs to intelligent, predictive layers below the surface. He emphasizes the rising importance of strategy, systems thinking, and invisible design. The article challenges UX designers to rethink their approach to AI-powered experiences and prepare for evolving user expectations around automation and adaptability.
O′Sullivan, M. (2025, January 30). Thinking like AI: A new approach to AI UX design. Microsoft Design. https://microsoft.design/articles/thinking-like-ai-a-new-approach-to-ai-ux-design/
O′Sullivan urges designers to consider AI not just as a feature, but as a collaborator with its own logic and needs. He advocates for “dual-perspective” design—aligning user experience with system behavior. This forward-thinking article is ideal for UX professionals designing adaptive, learning systems who want to balance human insight with machine reasoning for more responsive interactions.
Paschal, A. (2024, August 28). Where AI enhances UX design — and where it doesn′t. LogRocket Blog. https://blog.logrocket.com/ux-design/where-ai-does-doesnt-belong-ux-design/
Paschal critically explores when AI truly adds value in UX—and when it doesn′t. She highlights areas like testing, research, and content generation where AI boosts productivity, but cautions against overuse in complex emotional or ethical contexts. Designers will find this piece a level-headed guide for using AI thoughtfully while maintaining human-centered design priorities.
Stanford Encyclopedia of Philosophy. (2020). Ethics of artificial intelligence and robotics.
https://plato.stanford.edu/entries/ethics-ai/
This scholarly article outlines the major ethical concerns surrounding AI, including autonomy, consent, and accountability. UX designers can use it to deepen their understanding of ethical frameworks and apply them to real-world product design. It supports critical thinking about fairness, user rights, and the moral implications of automated decision-making in interactive systems.
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Here's the entire UX literature on Artificial Intelligence (AI) by the Interaction Design Foundation, collated in one place:
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