In Defense of the King (Content). By now you should have heard the meme that “content is king”; you’ve probably heard it frequently, in fact. This is because there is a remarkable amount of truth behind it. The copy, images, video and other miscellaneous content are the reason that your visitors are both going to and remaining on your site. Sites like Craigslist prove this to us time and again.
Design should be the extra layer, therefore adding to the experience that helps visitors locate wanted information, read said information, or simply accomplish a task. The very second you make the decision to place Lorem Ipsum into your mock-ups, you have done a great disservice to your design, content, website and users.
“By adding Lorem Ipsum to the design you are essentially dressing your king before you know his size.”
The problems will eventually have a domino effect on your site, sometimes without your awareness.
What are some of the bad things that have happened because of Lorem Ipsum? #
Lorem Ipsum has distracted clients from design mock-ups for decades, leading them to ask countless questions on why their site if filled with an unknown, foreign language.
It has often created confusion between the designer, developer and the copywriter or content provider.
It even gets pushed live sometimes without anyone noticing.
Just Another Visual Element
When you are designing with Lorem Ipsum, you diminish the importance of the copy by lowering it to the same level as any other visual element. The text simply becomes another supporting role, serving to make other aspects more aesthetic. Instead of your design enhancing the meaning of the content, your content is enhancing your design.
Here’s a sample of the dreaded Lorem Ipsum:
“Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer eu enim et eros posuere consequat id in libero. Aliquam pellentesque pulvinar aliquet. Curabitur sollicitudin sagittis consequat. In lobortis semper lacus et aliquet. Morbi luctus, tellus et imperdiet iaculis, ante eros sodales leo, in rutrum odio nunc at mi. Donec ac risus eu lorem vehicula sodales lacinia tempor ipsum. Curabitur sit amet quam leo. Donec faucibus posuere libero, a tincidunt elit auctor vitae. Vestibulum sed dui at erat lobortis sagittis a nec purus. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Etiam ultrices lacus ante.”
Tailored to Fit?
The person writing the content then gets lead down a dangerous path; they begin to write according to what has been laid out with Lorem Ipsum in the design. They effortlessly continue for paragraphs on matters that could easily be simplified into a single sentence. Content could be added that simply doesn’t need to be there.
Putting the Right Foot Forward
In an ideal situation, you would be working with a content specialist from the very beginning. Let’s face it, this doesn’t happen nearly as much as it should. Instead, the client is writing the copy for the website and it gets handed to you just before launch.
It’s time to reverse that thinking; you should be receiving and discussing the content even before the commencement of the design. When you are hired for the job, make it known to the client that you need the copy up–front and list the reasons why.
Still having problems? Write your own copy. Seriously, do it. This way when it comes to finalizing both the copy and the interaction, the copywriter will have an understanding of the point you were trying to make.
Now stand up. Put your left hand on your Mac or PC and the right one in the air and say: #
“I, [your name here] vow to never use Lorem Ipsum in my designs ever again.”
In Defense of the King (Content)
Thanks everyone for your input and arguments against. You’ve brought up some great points. It really crazy to think that fake Greek text is so controversial. That said, here is my rebuttal to some of the main points that have been brought up against my article.
The Title
The title is a bit extreme and was written to grab your attention. Lorem Ipsum will not kill your site design. What it does is it takes away some of the ability to provide the best possible solution for your design problem. You can of course still create great, beautiful, awesome designs with lorem ipsum but it takes away resources to solve your problem.
Change
I realize that this would not be an easy change both for you and for your clients. I am essentially suggesting you fix something that doesn’t seem broken. Just because using Lorem Ipsum is easy and it fits smoothly into your current process doesn’t make it right.
Having Final Copy
In no way should you have nor do you need final copy. First rough drafts work. It gives the designer and idea of what needs to be communicated. Once it is in the design or in code doesn’t make it final. Your copy should be a living working document that is continually changing along with your design.
If the copy becomes distracting in the design then you are doing something wrong or they are discussing copy changes. It might be a bit annoying but you could tell them that that discussion would be best suited for another time. At worst the discussion is at least working towards the final goal of your site where questions about lorem ipsum don’t.
Lorem Ipsum as a Tool
Lorem ipsum is more of a work around than a tool. It allows for your clients to be lazy and for you to have to deal with fake text. It’s only a tool because we HAVE to use it unlike other graphical tools like grids that enhance the design.
Dealing with Clients
I clearly didn’t cover this enough, That is my fault. I’ve found that when you describe your process up–front and tell them that you need at least an early draft of the copy, most clients are okay with that. I explain to them that through this process, they will get a better overall product. If they’re not okay with that, well, I have the luxury of ditching them. I would rather spend my time looking for clients that will work with me instead of against me, wasting my time fighting for a good product.
I almost removed this part before sending the article over because it has the potential to be very dangerous. I probably should have. It has been one of the ways that I have gotten around the client issue for headlines and sub–copy. I understand that we are trying to put food on the table and writing considerable amounts of body copy would be unreasonable. I also realize you are relying on your designer to know how to write, at least a very little bit, and that can be scary.
The point I tried to make is that you and your clients need to be thinking, understanding and gathering content before the design. Using Lorem Ipsum is a way to half–heartedly go about the most important part of your site.
Its been used in the print world for decades because they know exactly how many characters they can put on the page. Magazine or newspaper designers get early drafts of articles or at least a synopsis of the article before they start there work. I am suggesting you get the same.
The bottom line is design is an enhancement to your content. Nothing more. If you have an awesome design but junky content then no one will buy, use, read, or interact with it. This is the problem with a ton of Flash sites out there. If you have awesome content, users will find a way to work with the design (EhemCraiglist).
For those looking for extracurricular reading:
Adaptive Path | Death to Lorem Ipsum & Other Adventures in Content
Boxes and Arrows | Representing Content and Data in Wireframes
What are your thought about Lorem Ipsum? Do you think as designers, we should eliminate its use, or do you think that Lorem Ipsum still has its place in our design mock–ups? Have you had any unfortunate incidents that have happened with clients because you used Lorem Ipsum? We would love to hear what you say about it, so please do leave a comment below. Thank you for reading the article and we hope that you will share your opinion with us and to the rest of the design community.
When space and money are tight, launching an ecommerce business can feel like a moonshot. You can rack up costs by either manufacturing your own products or placing wholesale orders, waiting for sales to come through while your living room fills with product. And you’ll still need the time and funds to take product photos for your online store, market your brand, and hand-pack each box for shipping.
Luckily, there are two ecommerce business models to simplify your set-up: print-on-demand and dropshipping, two cost-efficient and space-saving ways to get your business going. Read on to compare the print-on-demand vs. dropshipping business models and decide which approach is best for you. What is dropshipping?
Dropshipping is a retail fulfillment method that allows you to launch an ecommerce brand without having to manufacture, buy, store, or ship inventory yourself. Instead, sellers use dropshipping suppliers—businesses that maintain and ship a large portfolio of products. By outsourcing order fulfillment, online business owners reduce overhead costs and the need for storage space.In a dropshipping business model, ecommerce business owners, or “sellers of record,” select products from a dropshipping supplier and list them in their online store. Once a customer places an order, the seller forwards it to the dropshipping supplier, who fulfills the order on the seller’s behalf. Shopify store owners can use powerful apps available in the Shopify App Store to source products from suppliers. Apps include DropCommerce for dropshipping in the US and Syncee for the EU and the rest of the world.
What is print on demand?
Print on demand (POD) is a type of dropshipping business model that allows you to sell products without purchasing or managing inventory, but with a custom-designed element.
In print on demand, sellers create a design, like custom artwork or a brand logo, then select products from a print-on-demand supplier to apply the design to. Apparel, accessories, mugs, and posters are popular print-on-demand products. The seller lists the products and artwork options in their online store, and once a customer places an order, the supplier prints, packages, and ships it to the customer. Shopify store owners can integrate with suppliers like Printful to easily start their POD businesses.Print on demand vs. dropshipping: similarities
No physical store or warehouse Limited inventory management Supplier reliability Low startup costs
Both dropshipping and print-on-demand business models shift the work of managing stock and fulfilling orders to third-party suppliers. Here’s how the models are similar: No physical store or warehouse
A key component of both dropshipping and print-on-demand models is the absence of a physical store. Suppliers ship products directly to the customer after an order has been placed, so there’s no need for you, the ecommerce business owner, to store it either. This is a large part of what makes it so easy for individuals to launch either style of business. The overhead costs associated with leasing, furnishing, and staffing a retail store or warehouse are nonexistent. Limited inventory management
Since both dropshipping companies and print-on-demand companies create, store, and ship products to customers, ecommerce business owners don’t need to devote resources to traditional inventory management. However, you can still follow inventory management best practices to improve your operations.
Use real-time inventory management tools to:
Sync with your suppliers and prevent stockouts Forecast demand and plan for increases or decreases Keep open lines of communication with suppliers to stay abreast of product trends and potential issues
Supplier reliability
Both print-on-demand and dropshipping businesses rely on suppliers not just for the products they sell, but for packing and shipping as well. Which is why it’s important to choose a supplier you trust to represent your brand experience, with high-quality products that you feel you can stand behind.
Before contracting with a supplier, ask about product samples and packaging to assess product quality yourself before listing them for sale. It may also be a good idea to have multiple suppliers on your roster, even for the same product, so a single supplier’s shortfall doesn’t impact your business. Low startup costs
By outsourcing supply and shipping, both models have far fewer startup costs than a traditional wholesale business, allowing you to direct more time and budget toward marketing, branding, and customer service.Print on demand vs. dropshipping: differences
Customization Product selection Competition Branding Return support
Though the order fulfillment flow of both models is almost identical, print on demand and dropshipping differ in a few ways: Customization
Print on demand relies on unique custom designs that translate your brand personality into tangible products. Some dropshipping suppliers offer custom packaging for business owners using branded dropshipping, but you can dropship products without doing any customization. The design is the product you’re responsible for in POD, while in dropshipping, you aren’t responsible for any product creation. Product selection
Dropshipping suppliers offer a wide variety of ready-to-ship products, often categorized into niches, like home goods or electronic accessories. Some suppliers, like DropCommerce, feature small independent brands and businesses in their portfolios.
Print-on-demand suppliers provide a variety of ready-to-customize products. Printful, for example, features a range of familiar, comfortable t-shirt brands that customers recognize, like Gildan or American Apparel, alongside blank tote bags, phone cases, and more. Competition
Chances are, your dropshipping supplier is providing products to multiple ecommerce stores. That means many stores may have the same products, or very similar ones, at roughly similar prices.
Because of this, ecommerce businesses relying on dropshipping benefit from an increased focus on merchant differentiation. This could mean curating unique product portfolios or creating a brand identity that draws customers to their site, rather than relying on the pull of the products alone.
Because print on demand applies a custom design to different products, the inventory is unique and more likely to find its niche. Branding
In print on demand, branding is front and center, since it’s a way for brands to create apparel and merchandise emblazoned with their logo, slogans, limited-edition capsules, and more. Dropshipping offers fewer branding opportunities. Packaging and delivery leave little room for personalization, and dropshipping products are rarely exclusive to a single retailer. Return support
Most dropshipping suppliers have return policies allowing refunds, exchanges, or returns, especially due to supplier error or damaged goods. Dropshipping products are selected from an existing catalog, so replacing them is relatively easy.
Because dropshipping stores are not in charge of inventory, they act as an intermediary between customers and suppliers, communicating policy details and order statuses as needed. Check with your supplier about returns and refunds, and shape your customer-facing policy accordingly.
By contrast, the custom nature of print-on-demand products makes returns tricky. Since each order is created as it comes in, some print-on-demand suppliers may require customers to cover the return cost or may not offer full refunds. This could require you, as the seller, to cover the cost of the return or exchange, for example, or enforce a strict return policy. Print on demand vs. dropshipping FAQ Is print on demand or dropshipping better?
Print on demand is a better fit if you want to sell your own custom products or branded merchandise, you’re brand-focused, and you enjoy the design process. Dropshipping is best suited to retailers who are sales-driven, not brand-focused, and skilled in marketing and driving traffic. Depending on your business, you may combine the two approaches. Is print on demand the same as dropshipping?
Print on demand is a type of dropshipping model. Dropshipping includes any arrangement where the supplier packages and ships goods as individual orders come in from the retailer’s online store. For example, you can dropship anything from shoes and soccer balls to portable vacuums and ring lights. POD specifically refers to businesses where custom items are printed based on incoming orders. These can include art prints, tote bags, and t-shirts customized with logos, branding, or illustrations. Is dropshipping more profitable than print-on-demand?
Though both models are low risk and have a low cost to enter, print-on-demand businesses can face less pricing competition. That’s thanks to the unique nature of POD products—if you have unique designs in mind, print on demand may be a profitable venture to consider.
Jesse James Garett, founder of Adaptive Path and author of the book The Elements of User Experience, speaks on what UX and UX design is, what UX looked like before and what are some of the challenges people are encountering now. He cites engagement as the main goal of UX design and, through some fantastic examples, shows that engagement is an universal quality achieved through visuality, sound, touch, smell, taste, body and mind. One of the most impressive moments from the session is when Jesse compares Beethoven to an experience designer, accompanied by the Ninth Symphony.
We’re all mostly accustomed to educating ourselves by reading articles. Rare are the opportunities to attend conferences or watch live shows on subjects that we’re interested in. That’s why we are presenting here phenomenal videos and related resources on the topic of user experience (UX) by different presenters at different events. We have focused on current content but have included some older videos that are still relevant. It will take you more than 16 hours to watch all of these videos. So, make some popcorn, turn off the lights and enjoy.
User Experience Videos
The State of User Experience Jesse James Garett, founder of Adaptive Path and author of the book The Elements of User Experience, speaks on what UX and UX design is, what UX looked like before and what are some of the challenges people are encountering now. He cites engagement as the main goal of UX design and, through some fantastic examples, shows that engagement is an universal quality achieved through visuality, sound, touch, smell, taste, body and mind. One of the most impressive moments from the session is when Jesse compares Beethoven to an experience designer, accompanied by the Ninth Symphony. Duration: 40 minutes.
UX Best Practices
In this excellent video session, Nick Finck pries open the most popular websites today, including eBay, Amazon, Toyota, Flickr, Twitter and Netflix, to explain user experience best practices. Nick discusses both the good and bad experiences on these websites on the basis of visual design, information architecture, interaction and ease of use. For those unfamiliar with UX, Nick explains the basic concept and contributing factors. The last 20 minutes of the session are reserved for Q&A, and some very interesting questions and great answers come up. Duration: 1 hour 32 minutes. There is no video of the session, only 75 slides with an audtio track.
5 Minutes on Imitation in Design
In this brilliant short talk, Jeffrey Veen explains what is meant by “Good artists copy, great artist steal.” By way of an interesting short story about World War II and the American army’s contact with natives of the Pacific Islands, he also explains “cargo cult” and how it still present today. A truly brilliant video. Duration: 6 minutes.
UX Team of One
In this half-hour session held at the IA Summit 2008, Leah Buley of Adaptive Path shows what it means to be a UX team of one by telling her own story and recounting a real-life example. Leah explains the concept of generative design, which is the process of creating and sketching a lot of different ideas and then refining them. The slides are amazing because Leah drew them by hand. Duration: 32 minutes.
Experience Is the Product
Peter Merholz explains why it is important to focus not on technology or features but on the product and the experience you want to create. The session is rich in good and bad examples drawn from real life. Duration: 43 minutes.
Behavior Is Our Medium
In this fantastic session, Robert Fabricant talks about interaction design beyond computing technology. His examples include the family tree and one particularly interesting tree that his daughters drew. His examples from history, especially ancient history, are amazing and put interaction design in a broader context beyond the computer interfaces we’re so used to. Duration: 61 minutes.
Web Form Design
If you think Web forms suck (because you have to fill them in every day), then you must see this video. This talk by Luke Wroblewsky at MIX 09 is based on years of experience in usability testing and study. Luke shows examples from the real world and addresses the major problems with Web form design today. You will see how some of the biggest websites have failed and what you can do to prevent such failures in your Web forms. Duration: 75 minutes.
User Experience Design for Non-Designers
In this short mini-breakout session, Shawn Konopinsky explains what UX design is at a very abstract level. He shows three different types of user interfaces and how UX design applies to all three. He explains basic concepts such as concept ideation, interaction design and visual design and emphasizes the importance of an “Agile” approach to projects. Duration: 18 minutes.
The Importance of Identity and Vision to UX Designers on Agile Projects
If you watched the previous video, you will know why the Agile approach is important in UX design. This video and the slides go deeper into the Agile approach, relying on research done at London university. Johanna Kollmann explains the research and the results. Duration: 32 minutes.
Learning IxD from Everyday Objects
“Inspiration surrounds us in everyday objects.” This is how Bill DeRouchey begins this brilliant talk about interaction design. We use icons, words or shapes to create a language of interactions. If we use the wrong language, the interaction becomes confusing, and this is what Bill shows, using many common objects and devices. One of the most interesting slides is the one that shows how users “hack the interface.” Duration: 45 minutes.
Two videos: Information and Architecture
In these two short videos, Mickey McManus explains related concepts: information and architecture, using everyday objects, such as cup of tea, to illustrate his argument. The videos are part of a short article in which Mickey draws these concepts together to explain what information architecture is. Duration: a few minutes.
Wireframes for the Wicked
Nick Finck, along with Michael Angels and Donna Spencer, goes over wireframing basics via short easy questions. What are wireframes? Why use wireframes? What does the process of wireframing look like? The three of them also explain the different types of wireframes and show examples of their work. Duration: 1 hour 5 minutes.
Design Prototyping: Bringing Wireframes to Life
Dan Harrelson explains the purpose of prototyping, why it is important and how it differs from wireframes and visual comps. With wireframing, instead of seeing how a concept functions or works, we see how it actually feels, which contributes to better usability testing. The video, like all videos from MIX 09, has “double view”: while looking at the large slides, you have a synchronized inset video of the speaker. Duration: 60 minutes.
Journey to the Center of Design
Does user-centric design really exist? In this funny keynote, Jared Spool asks whether user-centric design (UCD) has lost its way and needs to evolve into something else. Ironically, as studies have shown, many great projects have succeeded without UCD. Jared suggests three key attributes of great experience design that can replace UCD. Duration: 1 hour 14 minutes.
User-Centered Design 101
In this Web seminar, Frank Spiller demystifies the paradigm of UCD. He explains the difference between usability and UCD, which has confused many. From the perspective of the development process, he compares UCD to the Agile and Waterfall processes. At the end, he shows some UCD success stories. Duration: 58 minutes.
The Mint.com Experience
Aaron Forth of Mint.com speaks of the importance of aligning your user experience and business objectives. He goes over the challenges of delivering such a user experience at Mint, what the company did to attract brand interest and how Mint became one of the best personal-finance software tools today. Interesting fact: Mint discontinued IE6 support because it spent almost 20% of its time designing for the browser. Duration: 40 minutes.
Mobile User Experiences
Rachel Hinman start her talk about mobile user experiences with a provocative thought: that technology disengages us from real life. She says we should rethink our relationship to devices and use them in the context of concrete tasks. Duration: 24 minutes.
Tap Is the New Click
“Several years ago, Minority Report was a sci-fi movie, and now we’re already there with [the touch wall].” At the beginning of his session, Dan Saffer shows a couple of examples of touch-screen technology that marks the beginning of a new era of interaction in which gesture and touch replace mouse clicks. He explains how to design for these technologies by going over how touch-screens differ from traditional websites and devices. Duration: 67 minutes.
10/GUI
This is a fantastic experiment that examines current mouse-driven user interfaces and their limitation while offering a completely new way of interaction. Although keyboard and mouse were really great innovations in past century, Clayton Miller think we shouldn’t stop there but rather innovate further. Duration: 8 minutes.
The Least You Can Do About Usability
Steve Krug, known to many as the author of Don’t Make Me Think, here tries to convince people to do their own usability testing. Confirming Jacob Nielsen’s advice, that developers can do it themselves, Steve identifies the minimum requirements to make usability testing meaningful. At the end of the session, Steve performs a short usability test right in front of the audience, which makes this session truly outstanding. Duration: 60 minutes.
AJAX Usability
Kelly Norton talks about a very interesting, and often forgotten, subject: AJAX usability. He covers usability basics and how to design AJAX applications for humans. Full of real-life examples, he offers several concrete suggestions on how to improve usability in AJAX applications. Duration: 55 minutes.
Jason Fried of 37signals Speaking at Business of Software 2008
In this short amazing session, Jason Fried shares his thoughts and experiences from 37Signals. Ideas are agile, some even radical. This video is interesting because many of Jason’s thoughts contrast with widespread opinion. In the second half of the session, Jason uses audience questions to explain his ideas in more detail. Duration: 55 minutes.
Bonus videos
Sketching User Experiences
In this keynote from MIX09, the first 25 minutes are taken up by Bill Buxton, known to many as the author of the book Sketching User Experiences. In an optimistic talk, he explains why now, in these days of crisis, is a good time to pay attention to design and what key factors are required to revitalize the user experience. Duration: 25 minutes.
Interview With Don Norman on UX Week 2008
In a relaxed atmosphere at UX Week 2008, Don Norman answers interesting questions and share his thoughts on various aspects of design. One of the best bits comes at the beginning, when Don speaks of his “crusade” to replace the word “users” with “people.” Will we ever have “People Experience”? Do we already have it? Find the answer in this fantastic interview. Duration: 53 minutes.
In
a world where AI is informing more design choices, it’s easy to mistake
predictions for certainties. This article introduces Probabilistic
Design, a mindset that allows UX and product teams to accept
uncertainty, decipher AI outputs with nuance, and make smart, adaptive
decisions.
In 2024, an Air Canada customer asked a
chatbot about bereavement fares. The bot confidently gave him a refund
policy that didn’t exist. The airline refused to honor it. A tribunal
ruled in the customer’s favor. The bot hadn’t decided anything; it had
predicted an answer based on patterns in its training data. The company
treated that prediction as policy.
This is the risk at the heart of designing with AI today: probabilistic systems wrapped in deterministic interfaces. The AI offers a guess, the interface presents it as truth, and the user, or the organization, acts on it.
Humans
are wired for deterministic thinking. We prefer to believe that past
actions determine future outcomes. Flip a coin 999 times and get heads
every time, the deterministic mind assumes the coin is rigged. The
probabilistic mind accepts that the 1000th flip could still go either
way. That second mindset is harder to hold onto, but it is exactly what
designers need right now.
Products operate in complex, nonlinear
environments, and AI is accelerating that complexity. When designers and
product teams treat AI outputs as the answer rather than one of many
possible answers, they build fragile experiences, and in some cases,
like medical diagnostics or financial forecasting, genuinely dangerous
ones.
This article is a practical guide to designing probabilistically with AI as a partner.
It is about using AI to sharpen your thinking rather than outsource it,
accounting for model bias, human sentiment, and perceived risk along
the way.
Most
questions we ask AI do not produce binary answers. They produce
probabilities based on patterns in data. If you ask, “Do aliens exist?”
the answer will be somewhere between plausible and uncertain. Scientists
consider life elsewhere in the universe likely, but without any
concrete evidence, we cannot confirm it. The answer doesn’t resolve the
question; it frames it as a probability.
Designers should read AI outputs the same way. They are signals, not conclusions, possible outcomes that have to be interpreted within the context of product goals, user behavior, and business constraints.
Many digital products already work this way. Netflix doesn’t know you’ll enjoy Superstore because you watched The Office; it estimates the probability and surfaces the title accordingly. The interface is responding to a prediction.
Design
decisions can follow the same logic. AI models can combine behavioral
analytics with research insights to estimate the likelihood of certain
outcomes, and those probabilities can act as a yardstick for design
strategy. Consider a scenario where analytics suggest a 60% versus 90%
confidence that users will complete a purchase. At 60%, the design has
to do more persuasive work, testimonials, explanations, comparisons, and
reassurance signals may help the user move toward a decision. At 90%,
the user is already motivated, and the design should start removing
friction so the action can happen quickly. Same screen, very different
design problem.
Note: This is an oversimplification of the idea. Please be mindful of the intricate details of your product.
AI
can also simulate outcomes using historical data and behavioral models
before you commit to a direction. The value of those simulations depends
heavily on how prompts are structured, the context they define, the
hypothesis being tested, user motivation, and the edge cases you want
stressed.
I can think of one such practical use: evaluating early
designs through structured prompts, especially when you don’t have
direct access to the user group you’re designing for. The prompt below
is a starting point for evaluating a design from the perspective of
neurodivergent users as well. Treat it as a template, adapt the user
group, criteria, and output format to your product, and use it as a
conversation starter with your team rather than a verdict.
Evaluate
the [design file or weblink] for usability, accessibility, and content
relevance from the perspective of neurodivergent users such as those
with autism spectrum disorder, ADHD, learning disabilities, etc. Please consider the following criteria:
Is the layout and navigation intuitive for neurodivergent users?
Is the language and content appropriate and engaging for neurodivergent users?
Are there any barriers (technical, cognitive, or sensory) that this group might face when using the site?
How well does the site meet the specific needs or goals of neurodivergent users?
Provide a SWOT analysis, probability score for successful use by neurodivergent users, and any recommendations for improvement.
Note:
This is an oversimplification of the idea. Please be mindful of the
intricate details of your product and make any appropriate changes.
That said, simulations do not replace experimentation.
Because models are trained on historical data, they reflect past
behavior more strongly than they predict future change. Imagine
designing a voice interface for elderly users who struggle with
touchscreens. A model trained on mobile interaction data might predict
low engagement, not because the idea lacks value, but because the
dataset reflects different user behavior. Simulations should always
surface assumptions, not prevent innovation.
Be Cautious of Skewed Probabilistic Thinking Using AI
AI
systems are built on historical data, more specifically, on the
datasets they are trained on. That foundation shapes the outputs we
receive. During the AI Summit in France, India’s Prime Minister Narendra
Modi shared an example that illustrates this well. If you ask an AI
model to generate an image of a person writing with the left hand, the
output may still show a person writing with their right hand. The reason
is statistical: most people are right-handed, and the training data
reflects that. This may have improved over time, but the point remains
relevant. I still occasionally see this behavior when generating images
with similar models.
What you receive is not truth. It is the most statistically likely outcome
given the data available. Always ask whether past data meaningfully
predicts future behavior. If additional context can improve the
prediction, include it. Without context, the output is just one of many
possible answers dressed up as the only one.
Confidence
scores deserve the same scrutiny. Overtrusting a high-confidence output
leads to the Air Canada situation. Dismissing a low-confidence one can
cause teams to miss a real signal buried in noisy data. A prediction
with 90% confidence is not necessarily correct, and a 40% signal is not
necessarily useless. Designers must still weigh the possibilities,
consider the case in front of them, and bring judgment to what the AI
recommends.
Transparency is how you make that
possible. As AI systems increasingly shape decisions, people need
visibility into how outputs are generated, the sources, the reasoning,
and the summaries behind a recommendation. Black-box systems breed
distrust. Systems that reveal their reasoning let users evaluate outputs
for themselves. That transparency is good design and ethical practice.
It respects the trust people place in these tools.
Thinking in
probabilities often means resisting the temptation of quick answers. AI
can accelerate research and surface patterns faster than ever before,
but those outputs are starting points, not final decisions.
Practice Probabilistic Design with AI
Design
shapes how a product is ultimately experienced — the decisions
designers make determine whether the experience feels adequate,
intuitive, or exceptional. And design is inherently full of assumptions
and bets. Even the most rigorous research can yield multiple valid
solutions to the same problem, each carrying a different probability of
success.
Thinking probabilistically means recognizing that design
decisions rarely produce binary outcomes. They lead to a range of
possible results, and the role of the designer is to navigate those
possibilities and identify the path most likely to create value. This
mindset also builds adaptability: user needs evolve,
strategies change, and sometimes ideas fail. Teams that lean on data
signals, experimentation, and learning loops move faster toward the most
effective solution.
Before the practical principles, one fundamental idea:
Every
design decision is a bet, not a guarantee. Even when decisions are
informed by research and data, they are still based on smaller samples
and assumptions about how users will behave at scale. A well-researched
idea can still fail in the real world.
The Air Canada chatbot from
the introduction is a design lesson as much as a legal one. The bot was
doing what language models do, predicting plausible text. The
interface, however, communicated that prediction with complete
confidence, no caveats, no “here’s what our policy usually says,” no obvious path to a human. The user read confidence as commitment, and legally, so did the tribunal.
This
is what happens when probabilistic systems are wrapped in deterministic
interfaces. The interface transforms likelihood into certainty, and
that is where the risk emerges.
Designing for likelihood
means letting the interface continue to have uncertainty, visible
fallbacks to human support, and clear labeling when content is
AI-produced, preventing unforeseen issues.
Designers
should avoid binary thinking — a great idea does not mean guaranteed
success, and a familiar idea is not guaranteed to fail. Examine
variations, confidence levels, and edge cases instead. AI can certainly
help here, acting as a portfolio-thinking engine that surfaces different
interpretations, highlights risks, and generates structured
recommendations. The goal is not to optimize for certainty, but for
value: it should always be value-driven.
Think of the moment in Avengers: Infinity War
when Doctor Strange tells Tony Stark that out of millions of possible
futures, there is only one where they win. AI cannot tell you the
future, but it can help you explore the possible paths. Instead of
asking whether an idea will succeed, ask AI to estimate the likelihood and get a score, and use those signals to guide decisions.
Use Data as a Compass, Not a Map
Even
an actual probability is not a final answer. Imagine an AI model
predicts an 80% likelihood that users prefer a minimal checkout
experience. That does not mean the solution is simply “build a minimal
checkout.” Data should function as a compass, not a map.
Why did the model produce that prediction?
What data influenced it?
What assumptions is it leaning on?
What user behavior is it actually detecting?
These questions help designers validate predictions through usability testing
and additional research. AI excels at identifying patterns, but it
rarely explains why those patterns exist. Understanding motivation is
still a human-centered research task.
The clearest cautionary tale here is Amazon’s
experimental AI recruitment tool, which the company reportedly scrapped
after discovering that the model had learned to downgrade resumes from
women. The training data, roughly a decade of historical hiring
decisions, was skewed toward male candidates, and the model inherited
that skew. It began penalizing resumes that included the word “women’s,”
as in “women’s chess club captain,” and favoring language more commonly
found on men’s resumes. The system was not intentionally biased — the data
was. Amazon reportedly tried to adjust it and eventually shut the
project down because they could not guarantee it would not surface other
discriminatory patterns.
Examples like this are why interpreting AI output critically matters. Designers need to understand the data behind a prediction
and evaluate the reliability of the models they depend on. A
recommendation is only as good as the data it was trained on, and the
only way to know what that data is hiding is to ask.
Experiment as a Learning System
Experimentation
is usually framed as a way to validate a design decision. Want to lift
the click-through rate of a CTA? Run an A/B test. Probabilistic thinking
reframes this. Experiments should not only confirm solutions but also
reduce uncertainty.
Traditional approach: Testing features to confirm success.
Probabilistic approach: Testing assumptions to reduce uncertainty.
Traditional
A/B testing is expensive. It costs engineering time, traffic
allocation, and user exposure, especially when a losing variant runs
against a significant chunk of your audience. AI simulations can help
filter weaker ideas before they reach production by making
experimentation more efficient. User needs shift constantly, and the
most effective teams iterate fast.
AI can help evaluate
assumptions early by modeling potential outcomes based on historical and
behavioral data. These simulations act as a hypothesis filter, pointing
to the directions worth investing engineering effort in. This also
supports personalization — different users may respond better to
different experiences. Version A may resonate with high-intent users
while version B works better for exploratory ones. Multiple experiences
living side by side are not a flaw; they can be an intentional strategy.
AI
amplifies probabilistic thinking by surfacing scenarios, assigning
likelihood scores, and enabling personalization at scale.
Experimentation becomes a continuous feedback loop:
Predict → Test → Learn → Adjust → Repeat!
A few steps to make it work:
Shift the framing
So instead of saying: Will this feature succeed?
Ask: What assumptions are we testing?
Use this template to define the hypothesis:
We believe [behavioral assumption] will impact [metric] because [reason]. We’ll know we are right when [evidence].
Example:
We believe simplifying the onboarding flow from 5 steps to 3 will
increase completion rate because users experience decision fatigue when
too many choices are presented. We’ll know we’re right when we see at
least a 15% increase in step-to-step conversion with no drop in
activation rate.
AI simulations
Use AI to predict some of the assumptions.
Later, use the learning to identify the top candidates to test the hypothesis.
Embrace multi-versions
It is absolutely fine to have two live versions.
Fail fast
Reward learning vs success.
Normalize
smaller experimentations instead of a sweep of large changes. So
instead of taking on a risky bet, pick up a few probabilities and test
them.
Visualize probability
Create a probability table with probabilities of each variant and its prediction of success to keep track of all the changes.
One
of the hardest things for designers is making uncertainty
understandable and actionable. When uncertainty is hidden, users treat
AI outputs as facts. When it’s communicated clearly, trust increases.
Ranges,
estimates, and confidence indicators go a long way. A delivery window
of “Friday to Monday” tells the truth about variability without
misleading anyone, whereas a specific timestamp that slips erodes trust
every time. A face recognition feature that says “this looks like
Pratik, is that right?” sets more honest expectations than one that just
labels the photo with a name.
Communicating
uncertainty does not weaken trust — it strengthens it. The goal is not
to eliminate uncertainty but to design for it intelligently.
Different users respond to uncertainty differently, and your design should account for that:
User type
Risk
Design goal
Overtrusting users
They act too quickly and trust AI results easily./
Show uncertainty more prominently.
Distrustful users
They ignore AI entirely.
Show historical accuracy or confidence levels.
Skeptical/balanced users
Uses AI as a guide, not as a rule.
Reinforce AI assistance and let them decide the sort of framing.
Keep Humans In the Loop
AI
should augment human judgment, and certainly not replace it. The most
trustworthy systems are designed with clear moments where people can
review, challenge, correct, or override machine suggestions. Human-in-the-loop (HITL) is not a safety net — it is a refinement engine. Every override, correction, or rejection becomes high-quality feedback that improves the model over time.
Control
is a prerequisite for adoption. Users are more willing to rely on AI
when they understand how a suggestion was generated, can evaluate its
implications, and can easily intervene. Well-designed products make this
explicit: who is acting, what happens if the suggestion is wrong, and
where the user can step in.
These interactions are also critical
for system improvement. Every accept, reject, or edit is a strong
signal, and compared to passive analytics, this kind of feedback
produces far more meaningful training data. It closes the loop between
real-world usage and model performance.
What Does HITL Look Like in Practice?
GitHub
Copilot is a good everyday example. It offers inline code suggestions
that developers can accept with a tab, edit, or ignore entirely. The
system never commits code on the user’s behalf. Authorship stays with
the humans. Every data point becomes implicit feedback about which
suggestions were useful. Gmail’s Smart Compose works similarly,
presenting predicted text as optional, keeping tone and intent in the
user’s hands.
In higher-stakes contexts, HITL becomes more
explicit. Risk and fraud systems typically use probability scores to
route decisions: low-risk: proceed automatically; medium-risk: trigger
additional verification; and high-risk: escalate to a human reviewer.
This balances speed with judgment without removing oversight.
In
safety-critical domains like healthcare, human oversight is
non-negotiable. AI may flag anomalies or suggest a diagnosis, but the
clinician retains final authority. Tools that explain the details help
the practitioner understand why a recommendation was made, reinforcing confidence without removing accountability.
Designing for Human Judgment
From
a UX perspective, HITL is about matching the interaction pattern to the
level of risk. Simple accept/reject affordances work well for low-risk
suggestions that improve speed without real consequences. As the stakes
climb, impacting data, money, or people, preview and approval steps
become essential. Explanations help users calibrate trust rather than
blindly accept outputs.
What happens behind the scenes matters
just as much. The system should capture user decisions with context,
feed them into learning workflows, and log overrides for auditability.
Over time, teams can track signals like override rate, confidence
accuracy, time-to-approval, and perceived trust. A high override rate is
not a user failure. It is a signal that the design or the model needs
attention.
The Risk of Getting It Wrong
Poorly
implemented HITL systems can fail in subtle ways. Human review can
devolve into a rubber stamp. Workflows can slow down so much that users
route around the safeguards. Feedback can skew toward a narrow subset of
users. These risks are real, but they are design problems, not reasons
to remove HITL.
The goal is not to maximize human involvement. It
is to focus it where uncertainty, impact, or ethics demand it. Keeping
HITL is less about control and more about clarity: clarity about who decides, when uncertainty matters, and how responsibility is shared between people and machines.
Optimize for Resilience, Not Just Conversion
Good
design adapts as the landscape shifts. Product design, especially in
AI-powered systems, can no longer afford to optimize only for short-term
conversion metrics. User intent is fluid as well as ever-changing,
environments change rapidly, and probabilistic systems continuously
evolve too. What works today can quietly break tomorrow. Designing for
resilience means building products that stay reliable, trustworthy, and
useful even as assumptions, data, and user behaviors change.
Resilient design shifts the question from:
How do we maximize this metric right now?! → How does this system behave over time, under stress, and in uncertainty?
Do
not just consider last quarter’s numbers. Peek into the following
quarters to identify the shift and make changes accordingly.
Build Systems That Adapt as Probabilities Change
Likelihoods
shift constantly, AI models drift, contexts evolve, and user needs
mature as well, so designing as if conditions are stable creates
fragility in probabilistic environments. A resilient approach assumes volatility as the default.
Think
about how recommendation systems tend to evolve. The early version of a
content feed optimizes for engagement, and for a while, engagement goes
up. Then users start to notice the feed feels narrow, repetitive, maybe
even exhausting. Resilient systems rebalance, introducing novelty,
diversifying signals, and pulling in long-term satisfaction measures
alongside short-term clicks.
Designers should create
interfaces that expect change, dynamic re-ranking, contextual
explanations, and escape hatches from stale personalization loops, all
of which help systems stay useful as probabilities shift.
Optimize for Long-term Outcomes, Not Just Short-term Wins
Short-term
conversion gains often hide long-term costs. Speeding up onboarding can
reduce comprehension. Maximizing notification CTR can erode trust.
Optimizing engagement alone can produce unhealthy usage patterns.
Fragile systems maximize numbers while ignoring second-order effects,
the downstream consequences that show up weeks or months later.
Duolingo’s
hearts system is a good example of designing against this. It
introduces friction: if you make too many mistakes, you run out of
hearts and have to wait or practice older material to earn more. On
paper, that looks like a conversion killer: fewer lessons per session.
In practice, the team has publicly discussed how it supports long-term
motivation and retention, which is the metric that actually matters for a
learning app. Short-term engagement dips, but long-term outcomes
improve.
Meta has made a similar, if more reluctant, shift. The
company publicly acknowledged that optimizing purely for “time spent”
produced unintended emotional and societal effects, which led to a
stated pivot toward “meaningful social interactions” as a guiding
metric. Whether that shift fully landed is up for debate, but the
acknowledgment itself is the point: optimizing for the wrong thing at
scale has real downstream cost.
So, designers must routinely ask:
What behaviors are we unintentionally reinforcing?
Will this interaction still be healthy if repeated at scale?
Are we optimizing for the ecosystem’s wellbeing or just the next click?
Plan For Uncertainty the Way You Plan For Scale
Teams
routinely plan for traffic spikes, but rarely for uncertainty spikes.
Yet AI systems degrade, adversarial behaviors evolve, and external
shocks can reshape user behavior overnight. Resilient design assumes
variability and prepares for it.
This means designing for degrading confidence.
What does your interface do when the AI isn’t sure? Does it quietly
fail, or does it gracefully hand off? Does the experience still make
sense if AI assistance goes away entirely? A good fallback strategy is
as important as the happy path.
Some practical actions:
Design for degrading confidence. Show fallback states, allow manual overrides, and visualize uncertainty where it matters.
Measure long-term user health. Track satisfaction, retention quality, and unintended behavior, not just conversion.
Build adaptability in. Use adjustable ranking rules, dynamic states, and continual experimentation across segments.
Model second-order effects early. Every optimization casts a shadow; surface it before shipping.
Use a resilience checklist before launch. How does the system behave under low AI confidence? What’s the safe fallback? What drifts do we anticipate?
Conclusion
If you take one thing from this article into your next design review, make it this:
Stop asking “Will this work?” and start asking “How likely is this to work, and what happens when it doesn’t?”
That
single reframe changes how you write hypotheses, interpret AI output,
scope experiments, and design for the moments when the system is wrong.
Starting this week, name the assumption behind every AI recommendation
you accept, find one place in your product where a probabilistic output
is presented as a certainty, fix the framing, and design the fallback
before the happy path.
The shift from deterministic to probabilistic design is less about new tools and more about a new posture.
AI has not introduced uncertainty into our world. It has simply made
the uncertainty that was always there impossible to ignore. AI can
estimate, simulate, and recommend, but it cannot decide what matters,
which users are being overlooked, or which unconventional idea is worth
defending against a model trained on yesterday’s data. Those remain human responsibilities.
Think in ranges, not points. Test assumptions, not features. Build for
adaptation, not perfection. In a world where prediction is cheap, and
judgment is rare, the most valuable thing a designer can do is keep
asking, What else might be true?
Findings
from an exploratory user research study highlighting the unique
insights and practical UX recommendations shared by participants with
cognitive disabilities.
In the summer of 2024, I
became co-chair of a working group of expert researchers who came
together to determine how best to perform accessibility testing with
people with cognitive disabilities. This was work I did for Fable, where
I am currently VP of Innovation.
Cognitive disability is an
umbrella term for several disabilities that impact how people process
information, and it usually affects memory, focus, and/or learning. It
is the most prevalent disability in the U.S. (13.9% via CDC), and cognitive disability is increasing rapidly (Yale study).
We set four goals for ourselves to learn how to work with this audience:
How should we recruit and screen participants?
What are best practices for research with cognitive participants?
Do these methods work in a real study?
Documenting what we learned so that we could share it.
We
created a screener to recruit people who self-identified as having
challenges with memory, focus, and learning. We also reviewed published
studies that involved cognitive testers to learn best practices for
working with them.
Next, we tested these best practices with an
initial group of 25 testers in a pilot study. We fine-tuned our approach
iteratively and created a guide to running user interviews with
cognitive testers and a survey that could quantify their experiences
using digital products. Finally, we documented what we learned.
After
our pilot study with this new group of testers finished, I felt that
they would uncover more usability insights than the general population
(gen pop) user research participants I’d worked with in the past. I set
out to validate this hunch.
The Cognitive Usability Study
I
decided to run a joint study with Fable’s partners at the University of
California, Irvine, in collaboration with Syed Fatiul Huq and with help
from Fable researchers Pranav Pidathala, Ali Brown, and Michael Fagan
to see if my hypothesis about finding more insights with cognitive
testers proved true or not.
I generated three websites for the
study using an AI prototyping tool. I wanted three different types of
sites with different user goals and content so I could test a variety of
tasks in the study.
This
is a website for three-ingredient high-protein recipes. Recipes can be
browsed by category (vegan, muscle building, etc.). The site also
features blog posts about protein and contact information.
This
website is for a bookstore with a catalog of curated reads. It features
extensive filtering by book genre, a book swiping feature to build a
profile of likes and dislikes, custom book lists, a shopping cart, and
checkout.
A
website for a hair salon that allows you to book appointments and
consultations online. It has a VIP program and a variety of special
packages visitors can buy.
Design
Simple, brutalist, bright, lots of pictures.
Moody, classic, dark, lots of pictures of book covers.
Bold, clean, black and white with bursts of color.
Shopping cart, book matching, book lists, recommendations.
Appointment booking.
Tasks
Find a recipe for a high-protein snack.
Find a blog about protein and read it.
Find a way to be notified about new recipes and blog posts.
Find the book swiping feature and use it on 10 books.
Find the recommended book list.
Add books from two genres of your choice to cart.
Checkout the books in your cart.
Find the prices for getting a haircut.
Book a haircut appointment.
Find the price for the bridal package.
We
used a single screener with questions about memory, focus, and
learning, and screened participants into two groups based on whether
they self-identified as having cognitive challenges or not.
Cognitive
disability includes neurodiversity. Neurodivergent is an umbrella term
used to describe people whose brains process information and learn
differently. It is most commonly used for people who have learning
disabilities (e.g., Dyslexia), ADHD, and Autism.
We ran 30 user interviews, 10 per website, with an even 5⁄5
split between cognitive and gen pop participants for each website. In
each session, a participant completed all the tasks for one website
during an online user interview facilitated by one of the researchers
involved in the study.
All participants completed an Accessible Usability Scale (AUS) survey
at the end of their session. This is a free, Creative Commons-licensed
10-question survey to evaluate the usability of websites and mobile
apps.
Data Analysis Approach
I
reviewed all the study recordings and transcripts and made note of
every time a participant raised a concern, question, difficulty, or
asked a question about how something worked. I counted all of these as
issues. I also noted where a participant missed something that was part
of a task, even if they didn’t notice it themselves. I also noted every
suggestion for improvement made by participants.
Examples of issues found included:
Photo is too tall and requires a lot of scrolling to get to content (noted by participant).
I get no feedback when I like or dislike a book (noted by participant).
Participant missed the required P.O. Box checkbox the first time (observed by me).
Examples of suggestions included:
I would like to see a protein comparison in a table.
The “More information” tab should be moved up higher.
I would like more information on how the recommendation list is created.
Issues
and suggestions were counted once per participant, even if they
mentioned the same thing twice, but there are, of course, repeat issues
and suggestions across the different participants. It is expected in UX
research with multiple participants that you’ll find similar issues with
each participant, and that is a signal that an issue is a universal
challenge.
Findings Of The Cognitive Usability Study
Across the three websites tested:
Cognitive participants identified 197 issues.
Gen pop participants identified 113 issues.
Cognitive participants made 93 suggestions.
Gen pop participants made 54 suggestions.
Cognitive
participants surfaced more issues related to content, buttons, icons,
visual elements, and media than gen pop participants.
The
results aligned with my instincts: participants with cognitive
disabilities identified 1.8 times more issues and made 1.8 times more
suggestions than gen pop participants.
Let’s dive deeper into the
data for each website. Note that an AUS score ranges from 0 to 100, with
higher numbers representing better usability than lower numbers.
Table 2: Strong Snacks
This
site had the simplest design and content of all websites tested in the
study and accordingly had the lowest overall issues and the highest
median AUS scores. The data aligns with what you’d expect from an
easy-to-use and simple website.
On this website, cognitive
participants found 3.4 more issues and made 2.2 more suggestions on
average. Their average score of the overall experience was 13.7 points
lower than that of the gen pop participants.
Total issues
Average issues
Median issues
Total suggestions
Average suggestions
Median suggestions
Average AUS
Median AUS
Gen pop
32
6.4
6
13
2.6
2
90.5
97.5
Cognitive
49
9.8
9
24
4.8
4
76.8
73.0
Table 3: Turning Pages
This
was the website with the most varied functionality and the most tasks
to complete (4), so it’s not surprising that participants found the most
issues.
Here, cognitive participants found 6 more issues and made
3.2 more suggestions on average. They also scored the overall
experience 17.2 points lower than gen pop participants on average.
Total issues
Average issues
Median issues
Total suggestions
Average suggestions
Median suggestions
Average AUS
Median AUS
Gen pop
55
11
10
26
5.2
4
78.0
80.0
Cognitive
86
17
15
42
8.4
6
60.8
58.0
Table 4: Crown & Comb
This
website was intentionally designed to be complex, and task 3, finding
the bridal package, was meant to be extremely difficult to complete.
On
this last website, cognitive participants on average found 7 more
issues and made 2.4 more suggestions. Their average score for the
overall experience was 14.3 points higher than the gen pop participants.
Total issues
Average issues
Median issues
Total suggestions
Average suggestions
Median suggestions
Average AUS
Median AUS
Gen pop
26
5
4
15
3
3
49.5
35.0
Cognitive
62
12
11
27
5.4
2
63.8
68.0
Something
interesting happened with the AUS scores for cognitive and gen pop
participants in Tables 3 and 4. Cognitive participants scored Crown
& Comb higher than Turning Pages, but gen pop scored the opposite —
higher for Turning Pages and lower for Crown & Comb. If I had to
guess why, I suspect finding more issues on Turning Pages impacted the
cognitive participants’ perceptions of usability more than the gen pop
participants’.
The other major difference between the sites,
outlined in Table 5 below, was that cognitive participants found many
more issues with buttons and links on Turning Pages and more issues with
icons and visual elements on Crown & Comb. This suggests to me that
the interactions being challenging on Turning Pages were a more
significant challenge than issues with visual elements.
Qualitative Findings
When it comes to the more qualitative findings, I looked at trends in the types of issues found by both groups of participants.
Cognitive participants:
Were more likely to flag issues with icons or visual elements.
Surfaced problems with content more frequently.
Gave richer qualitative commentary, often explaining why something was hard to find or confusing.
Gen pop participants:
Were less likely to flag conceptual or comprehension barriers.
Gave shorter feedback, often stopping once the task was complete.
Table 5: Number Of Issues By Category
When
I grouped issues by category, the following issues surfaced more often
with cognitive participants: content, buttons and links (affordances and
function), icons or visual elements, and media (video, animations).
They nearly tied with gen pop participants on navigation issues (45 vs
46).
Strong Snacks
Turning Pages
Crown & Comb
Issue category
Gen pop
Cognitive
Gen pop
Cognitive
Gen pop
Cognitive
Content
11
22
11
30
23
36
Navigation
18
22
25
17
2
7
Buttons and links
0
5
7
20
3
0
Icons or visual elements
3
16
2
3
4
23
Media
0
2
0
1
0
0
Let’s
look at the commentary provided by one cognitive participant versus one
gen pop participant in the Crown & Comb sessions. The cognitive
participant gave an AUS score of 38, and the gen pop participant gave an
AUS score of 27.5. I chose to compare these two participants because
they both gave the lowest scores within their group.
Notice the
differences in how they described the overall experience in the quotes
below. The gen pop participant explained it was frustrating and not
engaging. The cognitive participant felt drained and less able to focus.
I interpreted the experience as having a more profound impact on the
cognitive participant’s overall wellbeing.
Gen pop participant quote
“As
soon as you have a name of a treatment and a little explanation and
like the duration and the price, as soon as you click onto that, it
should be that you can interact with that service straight away. And I
feel like if you're seeing a service repeated on a page multiple times
and you're still not able to select it, it's really, really frustrating.
This feels not particularly engaging.”
Cognitive participant quote
“For
example, like, the mental energy aspect of it, like, sometimes there's,
like, okay, cookies, and then ads, pop-ups, or maybe the website or
service has too many options to look through, and maybe I just want
something that I already know. I have to go through a lot of stuff. It
makes me, like, feel drained and less able to focus.”
In
summary, across all 3 websites we tested, participants with cognitive
accessibility needs identified 197 usability issues, compared with 113
identified by gen pop participants.
Cognitive participants made 93
suggestions for improving the user experience, compared with 54
suggestions by gen pop participants.
Cognitive
participants surfaced more issues related to content, buttons, icons,
visual elements, and media than gen pop participants.
How Cognitive Participants Benefit UX Research
In
working with cognitive participants for the last few years, I’ve seen
how they surface cognitive load issues consistently. These issues don’t
just impact people with cognitive disabilities such as neurodivergence;
they also impact:
Gen Z who lives in a world of short videos
optimized for attention-grabbing and struggles to focus on long-form and
written content.
Seniors who naturally experience cognitive decline as they age and have difficulty with complex interactions, especially online.
Adults
with jobs and families who are constantly busy, overloaded with
information, making their attention and focus difficult to grab.
What
would I have missed if I hadn’t included cognitive participants, and
how might that have impacted the business outcomes for these websites?
Strong Snacks
On the Strong Snacks website, the cognitive participants surfaced:
They would trust the content more if there were links to the sources of information, such as scientific journals.
The need for more context in headlines to understand what the blog is about.
Lack of clarity of the label “Add-ons.”
Layout
concerns where recipes for snacks interrupted the main article flow
instead of being placed in a sidebar with a distinct design.
How ads and animations can distract some users from reading the content.
Strong Snacks websit.
These
are improvements that would give all users more trust in the content
while also making it easier to read and skim for key content. The
research findings point towards design best practices, such as not
having continuous animation and using layout to draw attention to
different types of content that a senior designer might also point out.
Turning Pages
Without
cognitive participants, we might have missed the more subtle but
important issues with confusing interactions, such as how the “Add to
book bag” button worked. They were also confused about where reviews and
recommendations came from. Both of these issues could decrease a user’s
trust in the website.
Turning Pages website.
All
participants surfaced that the book-matching feature was hard to find,
but the deeper problem the cognitive participants emphasized is that the
site’s interactions don’t consistently behave in ways that they can
predict and understand, decreasing their confidence.
Anyone who
wants to buy a book could benefit from a clear understanding of how to
add books to a cart and complete the checkout quickly and with no
ambiguity. Compounded over hundreds or thousands of users, a lack of
clarity in a purchase flow will lead to lost revenue.
Crown & Comb
The Crown & Comb website in particular highlighted the benefits of having cognitive participants who raised:
Concern around why a service would be “subject to stylist consultation.”
Uncertainty with services that had similar labels but may or may not be the same service.
The importance of choosing a date being early in the flow for booking appointments.
Lack of clarity about when or how they would pay for services.
Crown & Comb website.
These
issues likely also affect gen pop participants, but they are more
likely to muddle through a task with incomplete information. However,
that can lead to losing customers to a better experience if a competitor
pops up. Loyalty is often tied to experiences, not just brands, and
having a poor experience means your customer retention can be weaker.
The
study showed that finding a bridal package was hard for everyone, but
the cognitive group showed how that became an accessibility barrier.
When you combine:
too much ambiguity,
too many decisions,
too little user feedback, and
too much effort to find something,
You
create a high enough cognitive load that some people will not be able
to complete the task. In my opinion, this is where usability issues
start to become accessibility barriers — when they increase cognitive
load so much that it becomes overwhelming for some users.
Key Takeaways
Include people with cognitive disabilities in user research, not just accessibility research. They
can surface general usability issues related to content, buttons and
links, icons or visual elements, and media while also helping you
understand how your product functions in terms of cognitive load.
Cognitive issues are both usability and accessibility issues. Tasks
that rely heavily on memory, focus, and decision-making can move along a
scale from difficult to impossible for some users to complete. That’s
where usability challenges become accessibility barriers.
Track more than task completion. Ask
users how they feel, how a task affects their energy, how distractions
impact their ability to focus, and how easy or hard a task was for them.
Start small and build your cognitive inclusive research practice over time. Even
a few sessions with people who have cognitive access needs can help you
better understand how to manage cognitive load for all users.
Start Incorporating Cognitive Insights Now
The
percentage of people aged 65 and older in America is projected to
increase from 17% to 25%. By 2060, 1 in 4 Americans will be an older
adult (U.S. Census).
This is where everyone starts to experience cognitive decline. As the
aging and cognitive population segment expands, companies will need to
build for these more complex user needs.
People with cognitive
access needs are a natural starting point because they will find the
types of usability issues that UX teams are used to. This could make
cognitive an easier entry point for inclusive research. Getting insights
from assistive technology users is still very important, but many teams
don’t know how to start doing that.
Cognitive accessibility is a
powerful on-ramp into broader accessibility research and testing. By
focusing first on cognitive load, clarity, and predictability, we build
research foundations that make future work on accessibility with screen
readers, screen magnifiers, and alternative navigation users more
approachable.
“2 sessions with cognitive users feel like 200 because of the volume of insights we get.”
—UX Manager at Bell Media
In
this small exploratory study, participants with cognitive disabilities
identified 1.8 times more issues and made 1.8 times more suggestions
than gen pop participants. I’ve seen this type of impact in research
conducted by Fable customers’ websites that aren’t AI-generated, too.
This
study had a relatively small sample size, so the findings are more
qualitative than quantitatively validated. Testing was also done on two
different platforms. Cognitive participant sessions were run using Fable Engage, and gen pop sessions were run on UserFeel.
Different platforms with unique participant panels can affect the
quality of insights and comfort levels with user research participation.
Disclosure:
I work for Fable and chose to use our platform because it was more
affordable than paying for access to another research platform, allowing
me to include more participants in the study at a lower cost.
Different
researchers facilitated the user interviews, which can also affect
findings, but all sessions used the same task structure and discussion
guide template, and all were completed online. Even though the sessions
were facilitated by different researchers, the issue and suggestion
counts were all done by me to ensure consistency across all websites and
participants.
Resources
I’ve compiled a few useful resources as you begin your cognitive inclusion journey.
