Moderated vs unmoderated research: which approach is best?

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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Knowing and understanding why and how your users use your product is invaluable for getting to the nitty gritty of usability. Delving deep with probing questions into motivation or skimming over looking for issues can equally be informative. 

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Put super simply, usability testing literally is testing how usable your product is for your users. If your product isn’t usable users often won’t complete their task, let alone come back for more. No one wants to lose users before they even get started. Usability testing gets under their skin and really into the how, why and what they want (and equally what they don’t).

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As we have been getting used to video calling regularly and using the internet for interactions, usability testing has followed suit. Being able to access participants remotely has allowed us to diversify the participant pool by not being restricted to those that are close enough to be in-person. This has also allowed an increase in the number of participants per test, as it becomes more cost-effective to perform remote usability testing.

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But if we’re remote, does this mean it can’t be moderated? No - remote testing, along with modern technology, can mean that remote testing can be facilitated and moderated. But what is the best method - moderated or unmoderated?

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What is moderated remote research testing?

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In traditional usability testing, moderated research is done in person. With the moderator and the participant in the same physical space. This, of course, allows for conversation and observational behavioral monitoring. Meaning the moderator can note not only what the participant answers but how and even make note of the body language, surroundings, and other influencing factors. 

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This has also meant that traditionally, the participant pool has been limited to those that can be available (and close enough) to make it into a facility for testing. And being in person has meant it takes time (and money) to perform these tests.

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As technology has moved along and the speed of internet connections and video calling has increased, this has opened up a world of opportunities for usability testing. Allowing usability testing to be done remotely. Moderators can now set up testing remotely and ‘dial in’ to observe participants anywhere they are. And potentially even running focus groups or other testing in a group format across the internet. 

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Pros of moderated remote research testing:

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- In-depth gathering of insights through a back-and-forth conversation and observing of the participants.

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- Follow-up questions don’t underestimate the value of being available to ask questions throughout the testing. And following up in the moment.

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- Observational monitoring noticing and noting the environment and how the participants are behaving, can give more insight into how or why they choose to make a decision.

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- Quick remote testing can be quicker to start, find participants, and complete than in-person. This is because you only need to set up a time to connect via the internet, rather than coordinating travel times, etc.

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- Location (local and/or international) Testing online removes reliance on participants being physically present for the testing. This broadens your ability to broaden the pool, and participants can be either within your country or global. 

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Cons of moderated remote research testing:

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- Time-consuming having to be present at each test takes time. As does analyzing the data and insights generated. But remember, this is quality data.

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- Limited interactions with any remote testing there is only so much you can observe or understand across the window of a computer screen. It can be difficult to have a grasp on all the factors that might be influencing your participants.

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What is unmoderated remote research testing?

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In its most simple sense, unmoderated user testing removes the ‘moderated’ part of the equation. Instead of having a facilitator guide participants through the test, participants are left to complete the testing by themselves and in their own time. For the most part, everything else stays the same. 

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Removing the moderator, means that there isn’t anyone to respond to queries or issues in the moment. This can either delay, influence, or even potentially force participants to not complete or maybe not be as engaged as you may like. Unmoderated research testing suits a very simple and direct type of test. With clear instructions and no room for inference. 

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Pros of unmoderated remote research testing:

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- Speed and turnaround,  as there is no need to schedule meetings with each and every participant. Unmoderated usability testing is usually much faster to initiate and complete.

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- Size of study (participant numbers) unmoderated usability testing allows you to collect feedback from dozens or even hundreds of users at the same time. 

- Location (local and/or international) Testing online removes reliance on participants being physically present for the testing, which broadens your participant pool.  And unmoderated testing means that it literally can be anywhere while participants complete the test in their own time.

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Cons of unmoderated remote research testing:

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- Follow-up questions as your participants are working on their own and in their own time, you can’t facilitate and ask questions in the moment. You may be able to ask limited follow-up questions.

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- Products need to be simple to use unmoderated testing does not allow for prototypes or any product or site that needs guidance. 

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- Low participant support without the moderator any issues with the test or the product can’t be picked up immediately and could influence the output of the test.

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When should you do moderated vs unmoderated remote usability testing?

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Each moderated and unmoderated remote usability testing have its use and place in user research. It really depends on the question you are asking and what you are wanting to know.

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Moderated testing allows you to gather in-depth insights, follow up with questions, and engage the participants in the moment. The facilitator has the ability to guide participants to what they want to know, to dig deeper, or even ask why at certain points. This method doesn’t need as much careful setup as the participants aren’t on their own. While this is all done online, it does still allow connection and conversation. This method allows for more investigative research. Looking at why users might prefer one prototype to another. Or possibly tree testing a new website navigation to understand where they might get lost and querying why the participant made certain choices.

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Unmoderated testing, on the other hand, is literally leaving the participants to it. This method needs very careful planning and explaining upfront. The test needs to be able to be set and run without a moderator. This lends itself more to wanting to know a direct answer to a query. Such as a card sort on a website to understand how your users might sort information. Or a first click to see how/where users will click on a new website.

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Planning your next user test? Here’s how to choose the right method

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With the ability to expand our pool of participants across the globe with all of the advances (and acceptance of) technology and video calling etc, the ability to expand our understanding of users’ experiences is growing. Remote usability testing is a great option when you want to gather information from users in the real world. Depending on your query, moderated or unmoderated usability testing will suit your study. As with all user testing, being prepared and planning ahead will allow you to make the most of your test.

Natalie and Lulu have forged a unique team culture that focuses on positive outputs (and outcomes) for their app’s growing user base. In doing so, they turned the traditional design approach on its head and created a dynamic and supportive team. 

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Natalie, Director of Design at Hatch, and Lulu, UX Design Specialist, recently spoke at UX New Zealand, the leading UX and IA conference in New Zealand hosted by Optimal Workshop, on their concept of “radical collaboration”.

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In their talk, Nat and Lulu share their experience of growing a small app into a big player in the finance sector, and their unique approach to teamwork and culture which helped achieve it.

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Background on Natalie Ferguson and Lulu Pachuau

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Over the last two decades, Lulu and Nat have delivered exceptional customer experiences for too many organizations to count. After Nat co-founded Hatch, she begged Lulu to join her on their audacious mission: To supercharge wealth building in NZ. Together, they created a design and product culture that inspired 180,000 Kiwi investors to join in just 4 years.

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Contact Details:

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Email: natalie@sixfold.co.nz

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LinkedIn: https://www.linkedin.com/in/natalieferguson/ and https://www.linkedin.com/in/lulupach/

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Radical Collaboration - How teamwork makes the dream work 💪💪💪

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Nat and Lulu discuss how they nurtured a team culture of “radical collaboration” when growing the hugely popular app Hatch, based in New Zealand. Hatch allows everyday New Zealanders to quickly and easily trade in the U.S. share market. 

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The beginning of the COVID pandemic spelled huge growth for Hatch and caused significant design challenges for the product. This growth meant that the app had to grow from a baby startup to one that could operate at scale - virtually overnight. 

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In navigating this challenge, Nat and Lulu coined the term radical collaboration, which aims to “dismantle organizational walls and supercharge what teams achieve”. Radical collaboration has six key pillars, which they discuss alongside their experience at Hatch.

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Pillar #1: When you live and breathe your North star

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Listening to hundreds of their customers’ stories, combined with their own personal experiences with money, compelled Lulu and Nat to change how their users view money. And so, “Grow the wealth of New Zealanders” became a powerful mission statement, or North Star, for Hatch. The mission was to give people the confidence and the ability to live their own lives with financial freedom and control. Nat and Lulu express the importance of truly believing in the mission of your product, and how this can become a guiding light for any team. 

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Pillar #2: When you trust each other so much, you’re happy to give up control

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As Hatch grew rapidly, trusting each other became more and more important. Nat and Lulu state that sometimes you need to take a step back and stop fueling growth for growth’s sake. It was at this point that Nat asked Lulu to join the team, and Nat’s first request was for Lulu to be super critical about the product design to date - no feedback was out of bounds. Letting go, feeling uncomfortable, and trusting your team can be difficult, but sometimes it’s what you need in order to drag yourself out of status quo design. This resulted in a brief hiatus from frantic delivery to take stock and reprioritize what was important - something that can be difficult without heavy doses of trust!

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Pillar #3: When everyone wears all the hats

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During their journey, the team at Hatch heard lots of stories from their users. Many of these stories were heard during “Hatcheversery Calls”, where team members would call users on their sign-up anniversary to chat about their experience with the app. Some of these calls were inspiring, insightful, and heartwarming.

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Everyone at Hatch made these calls – designers, writers, customer support, engineers, and even the CEO. Speaking to strangers in this way was a challenge for some, especially since it was common to field technical questions about the business. Nevertheless, asking staff to wear many hats like this turned the entire team into researchers and analysts. By forcing ourselves and our team outside of our comfort zone, we forced each other to see the whole picture of the business, not just our own little piece.

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Pillar #4: When you do what’s right, not what’s glam

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In an increasingly competitive industry, designers and developers are often tempted to consistently deliver new and exciting features. In response to rapid growth, rather than adding more features to the app, Lulu and Nat made a conscious effort to really listen to their customers to understand what problems they needed solving. 

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As it turned out, filing overseas tax returns was a significant and common problem for their customers - it was difficult and expensive. So, the team at Hatch devised a tax solution. This solution was developed by the entire team, with almost no tax specialists involved until the very end! This process was far from glamorous and it often fell outside of standard job descriptions. However, the team eventually succeeded in simplifying a notoriously difficult process and saved their customers a massive headache.

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Pillar #5: When you own the outcome, not your output.

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Over time Hatch’s user base changed from being primarily confident, seasoned investors, to being first-time investors. This new user group was typically scared of investing and often felt that it was only a thing wealthy people did.

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At this point, Hatch felt it was necessary to take a step back from delivering updates to take stock of their new position. This meant deeply understanding their customers’ journey from signing up, to making their first trade. Once this was intimately understood, the team delivered a comprehensive onboarding process which increased the sign-up conversion rate by 10%!

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Pillar #6: When you’re relentlessly committed to making it work

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Nat and Lulu describe a moment when Allbirds wanted to work with Hatch to allow ordinary New Zealanders to be involved in their IPO launch on the New York stock exchange. Again, this task faced numerous tax and trade law challenges, and offering the service seemed like yet another insurmountable task. The team at Hatch nearly gave up several times during this project, but everyone was determined to get this feature across the line – and they did. As a result, New Zealanders were some of the few regular investors from outside the U.S that were able to take part in Albirds IPO. 

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Why it matters 💥

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Over four years, Hatch grew to 180,000 users who collectively invested over $1bn. Nat and Lulu’s success underscores the critical role of teamwork and collaboration in achieving exceptional user experiences. Product teams should remember that in the rapidly evolving tech industry, it's not just about delivering the latest features; it's about fostering a positive and supportive team culture that buys into the bigger picture.

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The Hatch team grew to be more than team members and technical experts. They grew in confidence and appreciated every moving part of the business. Product teams can draw inspiration from Hatch's journey, where designers, writers, engineers, and even the CEO actively engaged with users, challenged traditional design decisions, and prioritized solving actual user problems. This approach led to better, more user-centric outcomes and a deep understanding of the end-to-end user experience.

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Most importantly, through the good times and tough, the team grew to trust each other. The mission weaved its way through each member of the team, which ultimately manifested in positive outcomes for the user and the business.

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Nat and Lulu’s concept of radical collaboration led to several positive outcomes for Hatch:

• It changed the way they did business. Information was no longer held in the minds of a few individuals – instead, it was shared. People were able to step into other people's roles seamlessly. 
• Hatch achieved better results faster by focusing on the end-to-end experience of the app, rather than by adding successive features. 
• The team became more nimble – potential design/development issues were anticipated earlier because everyone knew what the downstream impacts of a decision would be.

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Over the next week, Lulu and Nat encourage designers and researchers to get outside of their comfort zone and:

• Visit customer support team
• Pick up the phone and call a customer
• Challenge status quo design decisions. Ask, does this thing solve an end-user problem?

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