Radical Collaboration: how teamwork really can make the dream work

Introduction

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User Experience (UX) research has always been about people. How they think, how they behave, what they need, and—just as importantly—what they don’t yet realise they need. Traditional UX methodologies have long relied on direct human input: interviews, usability testing, surveys, and behavioral observation. The assumption was clear—if you want to understand people, you have to engage with real humans.

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But in 2025, that assumption is being challenged.

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The emergence of digital twins and synthetic users—AI-powered simulations of human behavior—is changing how researchers approach user insights. These technologies claim to solve persistent UX research problems: slow participant recruitment, small sample sizes, high costs, and research timelines that struggle to keep pace with product development. The promise is enticing: instantly accessible, infinitely scalable users who can test, interact, and generate feedback without the logistical headaches of working with real participants.

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Yet, as with any new technology, there are trade-offs. While digital twins may unlock efficiencies, they also raise important questions: Can they truly replicate human complexity? Where do they fit within existing research practices? What risks do they introduce?

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This article explores the evolving role of digital twins in UX research—where they excel, where they fall short, and what their rise means for the future of human-centered design.

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The Traditional UX Research Model: Why Change?

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For decades, UX research has been grounded in methodologies that involve direct human participation. The core methods—usability testing, user interviews, ethnographic research, and behavioral analytics—have been refined to account for the unpredictability of human nature.

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This approach works well, but it has challenges:

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1. Participant recruitment is time-consuming. Finding the right users—especially niche audiences—can be a logistical hurdle, often requiring specialised panels, incentives, and scheduling gymnastics.
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2. Research is expensive. Incentives, moderation, analysis, and recruitment all add to the cost. A single usability study can run into tens of thousands of dollars.
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3. Small sample sizes create risk. Budget and timeline constraints often mean testing with small groups, leaving room for blind spots and bias.
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4. Long feedback loops slow decision-making. By the time research is completed, product teams may have already moved on, limiting its impact.

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In short: traditional UX research provides depth and authenticity, but it’s not always fast or scalable.

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Digital twins and synthetic users aim to change that.

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What Are Digital Twins and Synthetic Users?

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While the terms digital twins and synthetic users are sometimes used interchangeably, they are distinct concepts.

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Digital Twins: Simulating Real-World Behavior

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A digital twin is a data-driven virtual representation of a real-world entity. Originally developed for industrial applications, digital twins replicate machines, environments, and human behavior in a digital space. They can be updated in real time using live data, allowing organisations to analyse scenarios, predict outcomes, and optimise performance.

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In UX research, human digital twins attempt to replicate real users' behavioral patterns, decision-making processes, and interactions. They draw on existing datasets to mirror real-world users dynamically, adapting based on real-time inputs.

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Synthetic Users: AI-Generated Research Participants

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While a digital twin is a mirror of a real entity, a synthetic user is a fabricated research participant—a simulation that mimics human decision-making, behaviors, and responses. These AI-generated personas can be used in research scenarios to interact with products, answer questions, and simulate user journeys.

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Unlike traditional user personas (which are static profiles based on aggregated research), synthetic users are interactive and capable of generating dynamic feedback. They aren’t modeled after a specific real-world person, but rather a combination of user behaviors drawn from large datasets.

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Think of it this way:

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• A digital twin is a highly detailed, data-driven clone of a specific person, customer segment, or process.
• A synthetic user is a fictional but realistic simulation of a potential user, generated based on behavioral patterns and demographic characteristics.

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Both approaches are still evolving, but their potential applications in UX research are already taking shape.

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Where Digital Twins and Synthetic Users Fit into UX Research

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The appeal of AI-generated users is undeniable. They can:

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• Scale instantly – Test designs with thousands of simulated users, rather than just a handful of real participants.
• Eliminate recruitment bottlenecks – No need to chase down participants or schedule interviews.
• Reduce costs – No incentives, no travel, no last-minute no-shows.
• Enable rapid iteration – Get user insights in real time and adjust designs on the fly.
• Generate insights on sensitive topics – Synthetic users can explore scenarios that real participants might find too personal or intrusive.

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These capabilities make digital twins particularly useful for:

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• Early-stage concept validation – Rapidly test ideas before committing to development.
• Edge case identification – Run simulations to explore rare but critical user scenarios.
• Pre-testing before live usability sessions – Identify glaring issues before investing in human research.

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However, digital twins and synthetic users are not a replacement for human research. Their effectiveness is limited in areas where emotional, cultural, and contextual factors play a major role.

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The Risks and Limitations of AI-Driven UX Research

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For all their promise, digital twins and synthetic users introduce new challenges.

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1. They lack genuine emotional responses.
   AI can analyse sentiment, but it doesn’t feel frustration, delight, or confusion the way a human does. UX is often about unexpected moments—the frustrations, workarounds, and “aha” realisations that define real-world use.
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2. Bias is a real problem.
   AI models are trained on existing datasets, meaning they inherit and amplify biases in those datasets. If synthetic users are based on an incomplete or non-diverse dataset, the research insights they generate will be skewed.
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3. They struggle with novelty.
   Humans are unpredictable. They find unexpected uses for products, misunderstand instructions, and behave irrationally. AI models, no matter how advanced, can only predict behavior based on past patterns—not the unexpected ways real users might engage with a product.
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4. They require careful validation.
   How do we know that insights from digital twins align with real-world user behavior? Without rigorous validation against human data, there’s a risk of over-reliance on synthetic feedback that doesn’t reflect reality.

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A Hybrid Future: AI + Human UX Research

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Rather than viewing digital twins as a replacement for human research, the best UX teams will integrate them as a complementary tool.

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Where AI Can Lead:

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• Large-scale pattern identification
• Early-stage usability evaluations
• Speeding up research cycles
• Automating repetitive testing

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Where Humans Remain Essential:

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• Understanding emotion, frustration, and delight
• Detecting unexpected behaviors
• Validating insights with real-world context
• Ethical considerations and cultural nuance

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The future of UX research is not about choosing between AI and human research—it’s about blending the strengths of both.

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Final Thoughts: Proceeding With Caution and Curiosity

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Digital twins and synthetic users are exciting, but they are not a magic bullet. They cannot fully replace human users, and relying on them exclusively could lead to false confidence in flawed insights.

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Instead, UX researchers should view these technologies as powerful, but imperfect tools—best used in combination with traditional research methods.

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As with any new technology, thoughtful implementation is key. The real opportunity lies in designing research methodologies that harness the speed and scale of AI without losing the depth, nuance, and humanity that make UX research truly valuable.

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The challenge ahead isn’t about choosing between human or synthetic research. It’s about finding the right balance—one that keeps user experience truly human-centered, even in an AI-driven world.

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This article was researched with the help of Perplexity.ai. 

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If there’s one universal truth in user research, it’s that at some point you’re going to need to find people to actually take part in your studies. Be it a large number of participants for quantitative research or a select number for in-depth, in-person user interviews. Finding the right people (and number) of people can be a hurdle.

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With the right strategy, you can source exactly the right participants for your next research project.

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We share a practical step-by-step guide on how to find participants for user experience research.

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The difficulties/challenges of user research recruiting 🏋️

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It has to be acknowledged that there are challenges when recruiting research participants. You may recognize some of these:

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• There are so many channels and methods you can use to find participants, different channels will work better for different projects.
• Repeatedly using the same channels and methods will result in diminishing returns (i.e. burning out participants).
• It’s a lengthy and complex process, and some projects don’t have the luxury of time.
• Offering the right incentives and distributing them is time-consuming.
• It’s hard to manage participants during long-term or recurring studies, such as customer research projects.

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We’ll simplify the process, talk about who the right participants are, and unpack some of the best ways to find them. Removing these blocks can be the easiest way to move forward.

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Who are the right participants for different types of research? 🤔

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1. The first step to a successful participant recruitment strategy is clarifying the goals of your user research and which methods you intend to use. Ask yourself:

• What is the purpose of our research?
• How do we plan to understand that?

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2. Define who your ideal research participant is. Who is going to have the answers to your questions?

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3. Work out your research recruitment strategy. That starts by understanding the differences between recruiting for qualitative and quantitative research.

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Recruiting for qualitative vs. quantitative research 🙋🏻

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Quantitative research recruiting is a numbers game. For your data analysis to be meaningful and statistically significant, you need a lot of data. This means you need to do a lot of research with a lot of people. When recruiting for quantitative research, you first have to define the population (the entire group you want to study). From there, you choose a sampling method that allows you to create a sample—a randomly selected subset of the population who will participate in your study.

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Qualitative recruiting involves far fewer participants, but you do need to find a selection of ‘perfect’ participants. Those that fit neatly into your specific demographic, geographic, psychographic, and behavioral criteria relevant to your study. Recruiting quality participants for qualitative studies involves non-random sampling, screening, and plenty of communication.

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How many participants do you need? 👱🏻👩👩🏻👧🏽👧🏾

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How many participants to include in a qualitative research study is one of the most heavily discussed topics in user research circles. In most cases, you can get away with 5 people – that’s the short answer. With 5 people, you’ll uncover most of the main issues with the thing you’re testing. Depending on your research project there could be as many as 50 participants, but with each additional person, there is an additional cost (money and time).

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Quantitative research is obviously quite different. With studies like card sorts and tree tests, you need higher participant numbers to get statistically meaningful results. Anywhere from 20 - 500 participants, again coming back to the purpose of your test and your research budget. These are usually easier and quicker to implement therefore the additional cost is lower.

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User research recruitment - step by step 👟

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Let’s get into your research recruitment strategy to find the best participants for your research project. There are 5 clear steps to get you through to the research stage:

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1. Identify your ideal participants

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Who are they? What do they do? How old are they? Do they already use your product? Where do they live? These are all great questions to get you thinking about who exactly you need to answer your research questions. The demographic and geographic detail of your participants are important to the quality of your research results.

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2. Screen participants

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Screening participants will weed out those that may not be suitable for your specific project. This can be as simple as asking if the participants have used a product similar to yours. Or coming back to your key identified demographic requirements and removing anyone that doesn’t fit these criteria.

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3. Find prospective participants

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This is important and can be time-consuming. For qualitative research projects, you can look within your organization or ask over social media for willing participants. Or if you’re short on time look at a participant recruitment service, which takes your requirements and has a catalog of available persons to call on. There’s a cost involved, but the time saving can negate this. For qualitative surveys, a great option can be a live intercept on your website or app that interrupts users and asks them to complete a short questionnaire.

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4. Research incentives

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In some cases you will need to provide incentives. This could be offering a prize or discount for those who complete online qualitative surveys. Or a fixed sum for those that take part in longer format quantitative studies.

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5. Scheduling with participants

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Once you have waded through the emails, options, and communication from your inquiries make a list of appropriate participants. Schedule time to do the research, either in person or remotely. Be clear about expectations and how long it will take. And what the incentive to take part is.

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Tips to avoid participant burnout 📛

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You’ve got your participants sorted and have a great pool of people to call on. If you keep hitting the same group of people time and time again, you will experience the law of diminishing returns. Constantly returning to the same pool of participants will eventually lead to fatigue. And this will impact the quality of your research because it’s based on interviewing the same people with the same views.

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There are 2 ways to avoid this problem:

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1. Use a huge database of potential participant targets.
2. Use a mixture of different recruitment strategies and channels.

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Of course, it might be unavoidable to hit the same audience repeatedly when you’re testing your product development among your customer base.

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Wrap up 🌯

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Understanding your UX research recruitment strategy is crucial to recruiting quality participants. A clear idea of your purpose, who your ideal participants are, and how to find them takes time and experience. 

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And to make life easier you can always leave your participant recruitment with us. With a huge catalog of quality participants all at your fingertips on our app, we can recruit the right people quickly.

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Check out more here.

UI and UX are two terms that are often used interchangeably and confused for one another, but what do they actually mean? And is there a crossover between them?

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These two terms have only grown in use in recent years, thanks largely to the exploding technology sector. This is great news. For organizations, effectively harnessing UX and UI enables them to build products and services that people will actually want to use – and continue using. For users, they’ll have access to products designed for them. 

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What is UX? 🤳🎯

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User experience (UX as it’s commonly called) refers to the experience that a person has with a product or service. 

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We can determine whether a user experience is good or bad based on how easy (or difficult) it is for users to interact with the various elements of a product or service. Is the sign-up flow easy to use? Does the CTA button on the homepage encourage users to click? UX design exists to answer questions like these – and here’s how.

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At the core of UX design is user research, which you can use to understand customer pain points and actually build products designed for the people using them. Typically, user research involves the use of a number of different research methods designed to answer specific questions. Card sorting, for example, can show you how people think the information on your website should be arranged.

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Designer and information architect Peter Morville came up with the user experience honeycomb, which demonstrates the various components of UX design.

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Don Norman of Nielsen Norman Group defines UX as “[encompassing] all aspects of the end-users interaction with the company, its services, and its products”.

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If this seems broad, that’s because it is. UX actually extends beyond just the digital products of an organization and can be used for areas like retail, customer service and more. In fact, there’s actually a growing movement to replace UX with customer experience (CX), as a way of encompassing all of these disparate elements.

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What is UI? 🪄📲

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User interface (UI), in the most stripped-back definition, is the interface by which a user and a computer system communicate with one another. This includes the touchscreen on your smartphone, the screen on your laptop, your mouse and keyboard and countless other mechanisms.

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With this in mind, user interface design is focused on the elements that users will see on these interfaces, such as buttons, text and images. UI design is all about layout, look and feel. The objective of UI design is to visually guide users through an interface so they can complete their task. In a nutshell, you don’t want a user to think too hard about what they’re doing.

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UI has its origins in the 1980s, when Xerox developed the very first graphical user interface (GUI). Instead of needing to interact with a computer through a programming language, people could now use icons, menus and buttons. The rest, as they say, is history. Apple came along with the Macintosh computer in 1984 (bringing with it the first point and click mouse), and now we’re all carrying smartphones with touch screens that even a baby can operate.

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Like UX, UI has grown significantly – going far beyond what you’ll see on a computer screen. Those involved in the field of UI design today will work as much on the interfaces of computer programs and apps as they will on the user interfaces of cars, wearable devices and technologies in the home. If current trends continue, UI design is likely to become an even bigger field in the years ahead.

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What’s the difference between UX and UI? 👀

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UX and UI are both essential components of a product or service. You can’t have one without the other, and, as we’ve explored, neglecting one could have serious consequences for your product’s success.

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The difference between UX and UI is that UX is focused on the experience of using something and UI is focused on the look and feel of the interface. 

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“User Experience (UX) and User Interface (UI) are some of the most confused and misused terms in our field. A UI without UX is like a painter slapping paint onto a canvas without thought; while UX without UI is like the frame of a sculpture with no paper mache on it. A great product experience starts with UX followed by UI. Both are essential for the product’s success”. - Rahul Varshney, co-creator of Foster.fm

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The difference between UX and UI is that UX is focused on the experience of using something and UI is focused on the look and feel of the interface. 

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Or, if you’d prefer a statement from venerable Nielsen Norman Group: “It’s important to distinguish the total user experience from the UI, even though the UI is obviously an extremely important part of the design. As an example, consider a website with movie reviews. Even if the UI for finding a film is perfect, the UX will be poor for a user who wants information about a small independent release if the underlying database only contains movies from the major studios”.

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With this in mind, let’s now take a look at the people behind UX and UI. What do the roles look like in these fields? And, more importantly, what do they involve?

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UX and UI jobs guide 📱🧑🏻💻

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• Visual designer: This role works with other design roles in the organization (brand, marketing, etc) to ensure designs match brand guidelines. Visual designers also work with UX designers to verify that designs meet accessibility and usability requirements.
• UX strategist: At the core, a UX strategist should act as a champion of good UX. That is to say, work to ensure the principles of usability and human-centered design are well understood and utilized. They should also assume some of the responsibility of product-market fit, and work with product managers and the ‘business’ side of the organization to mesh business requirements with user requirements.
• UX designer: The most common UX profession, UX designers should have a strong understanding of the principles of UX design as well as some research ability. Essentially a jack of all trades, the UX designer will float between all stages of the UX lifecycle, helping out with usability tests, putting together prototypes and working with other areas of the organization.
• Service designer: The service designer looks at the entire end-to-end process and works with other designers, pulling them when required to liaise on visual designs and UI work. In a smaller organization, the responsibilities of this role will typically be absorbed by other roles, but eventually, there comes a time for the service designer. 

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Wrap up 🎬

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UX and UI as terms are only going to continue to grow, especially as technology and technology companies continue to proliferate across the globe. If you want to make sure that the user experience and user interfaces of your product or service are fit for the people using them, there’s no better place to start than with user research using powerful tools.