Synthetic User Testing: UX Research with AI Personas

Stakeholders often need user feedback to approve a new product interface. But what if you just don’t have the time to recruit a panel? It’s the familiar kind of pressure that's pushing product teams toward a radically different approach to usability testing – one without human participants, called synthetic user testing.

Synthetic user testing uses AI participants, built on vast datasets spanning consumer demographics, psychographics, and behavioral patterns, to approximate how real people navigate, react to, and experience digital products, designs, or interfaces. Unlike traditional research, it takes only a few minutes to get meaningful, human-like feedback.

Currently, synthetic users are being used by brands to test prototypes, flag friction points, and simulate user interactions on demand. In this article, we'll list the full range of use cases, from concept validation to pre-launch stress testing, and discuss how you can conduct synthetic user testing with Delve AI.

Now, What Is a Synthetic User?

Before we get into synthetic user testing, we need to talk about synthetic users. By definition, synthetic users are AI-generated personas or digital avatars, built using artificial intelligence and machine learning systems, that replicate real user behaviors, preferences, and decision-making processes. They're trained on actual customer data and can answer research questions, sit through an interview (not literally), and take usability tests like real people.

Right now, simulated users, from platforms like Delve AI, can be used to run:

• quick quantitative surveys
• in-depth, qualitative interviews
• structured focus group discussions

They promise to cut through the nightmares of user testing, including but not limited to participant recruitment, scheduling conflicts, budget overruns, and geographic limitations. You basically get quality feedback at a fraction of the cost and time of traditional user research.

(Synthetic user panel for a marketing agency generated using Delve AI)

Of course, most UX researchers, forums, and communities online (like r/UXResearch on Reddit) don’t think much of it. We’ll get to the why behind these sentiments later.

How do synthetic users work?

Synthetic users, or synthetic personas, run on large language models trained on user behavioral datasets to understand product scenarios in context, work through problems, and offer constructive feedback.

The creation process is simple enough: Behavioral data analysis → generative AI simulations → synthetic users.

Behavioral analysis is used to mine large volumes of real user actions, preferences, browsing habits, and decision sequences to build a model of how different types of people interact with digital products. From there, the system relies on generative models to produce realistic scenarios.

There are two types of models. Generative Adversarial Networks (GANs) use a pair of neural networks – one generates synthetic behavior data, the other evaluates it against real-world patterns. Variational Autoencoders (VAEs) learn from observed interaction patterns to generate new data that follows the same patterns.

These models power the simulation layer, where synthetic users are assigned profiles and start replicating interactions – clicks, scrolls, form submissions, navigation choices – that align with their personas’ characteristics. Once deployed, these AI participants can interact with different screens in real time. So, you get to observe how a user is likely to behave in a specific situation, whether that's navigating a multi-step checkout, completing a demo request form, or encountering an error, without waiting for a real user session.

Synthetic users, panels, AI panels, and digital twins

There are a couple of terms that get thrown around in the synthetic research space. Yet not every AI-based testing method works the same; hence, you need to know what makes one different from the other.

Currently, we have:

• Customer GPTs
• Synthetic users
• Synthetic panels
• Digital twins
• AI agents

The most basic of the lot are customer GPTs built using platforms like ChatGPT. They are custom-built AI models designed around a target persona. You define the profile, and the model responds to your product as that person would.

Synthetic users, in the stricter sense, are LLM-powered models prompted to role-play as individual personas or groups. They're useful for simulating qualitative research, like the kind of exploratory feedback you'd normally get from ethnographic studies. Similarly, synthetic panels mirror the demographic makeup of a real research audience and let you replicate large-scale research results.

Digital twins, like the ones by Delve AI, are virtual replicas or avatars of existing personas. You can interact with them to predict how a specific segment will respond to changes in your product.

(Customer digital twin interaction sample for a Peruvian telecom company)

Now, AI agents are a bit more complex. They are autonomous entities programmed to carry out tasks, make decisions, and adapt to their environment like a real person might to stress-test complex workflows or multi-step user journeys.

Each of these methods serves a different purpose, and the right choice depends on whether you need quick feedback or detailed audience interactions.

What is Synthetic User Testing?

Synthetic user testing uses simulated users to mirror real people and deploy them across your product to get insights at every stage of the lifecycle, replacing traditional usability studies. Here, synthetic users walk through complete flows, scrolling, clicking, and buying products, as members of a specific demographic or behavioral segment.

Teams can then use this data to validate prototypes, audit UI intuitiveness, and locate the exact moments where users lose momentum. For example, e-commerce brands can map the full path from product discovery to checkout and spot the steps that trigger cart abandonment with synthetic respondents.

A simple AI testing platform involves the following steps, wherein you:

1. Select the synthetic audiences you want feedback from
2. Add the goals and tasks you want the users to complete
3. Upload your design prototype or enter the site URL

Once done, the synthetic respondents go through your design, complete the given tasks, and simulate other interactions. They don’t follow a set script but make dynamic decisions based on empirical behavioral patterns. Outputs generally include full thought transcripts, heatmaps, task success rates, and critical usability issues.

You get to know your users’ thought processes, goals, and motivations at every touchpoint and spot obvious usability and functionality problems before committing to full-blown human research studies.

What tests can you run with synthetic users?

With a proper synthetic research software, you can generate hundreds of AI participants to answer questions and simulate edge case scenarios. Right now, you can use them as a first filter to test early-stage prototypes and conduct A/B tests, comprehension checks, journey-level testing, and more.

To exemplify, let's take the case of Growth Designers. The team ran a synthetic user test for their own website and simulated a first-time visitor who wants to join the design community. The feedback was instantaneous:

• Synthetic participants flagged that "growth design" is never defined above the fold.
• The Slack join flow feels unnecessarily gated.
• The newsletter subscription form gives zero context on what you're signing up for.
• The School page buries pricing and curriculum below multiple CTAs.

As you can see, none of these are problems that UX researchers wouldn’t be able to spot on their own. But AI users do it in a little less than 3 minutes, with zero recruitment time. The verdict here? Synthetic testing is a great way to catch obvious problems and friction points early. However, you still need people for the not-so-obvious problems.

In UX testing, you can further use synthetic users to:

• Run simulations to develop a hypothesis about your audiences
• Test design variants and run through different flows at the review stage
• Get reactions to onboarding sequences, feature descriptions, and in-app instructions
• Generate synthetic responses to find objections, preferences, and confusion points

You can shortlist flows, flag clarity and navigation issues, and get directional sentiment data before fielding a formal study.

A plus point is that synthetic users operate on anonymized, aggregated data rather than individual customer data, so teams can run measurable experiments across the user journey without navigating the compliance overhead that comes with personal user data.

Benefits of AI synthetic user testing: Speed, cost, and faster feedback loops

The top benefits that come to mind are speed, cost, and continuous feedback loops. Think about it: a normal A/B test can take weeks before you see meaningful results, and it’s expensive to repeat. Synthetic users reduce that timeline and deliver comparable insights in hours. UX teams can continuously run tests on every variation and correct issues while they’re easy to fix.

Synthetic user panels can also scale to hundreds of simulated participants and be leveraged to validate existing research at near-zero marginal cost. It's cheap too, costing approximately $99 per hundred AI research participants (or ~ $0.99 per user). This scalability, combined with the ability to simulate any kind of human interaction, opens the door to testing edge cases and higher-risk flows that would otherwise be too costly, slow, or impossible to validate.

Synthetic respondents are additionally free of the biases inherent in real participants. Human testers often bring an implicit familiarity with common UX flows. But synthetic respondents interact with your interface exactly as built, which means the problems they flag are likely to be ones your first-time users will encounter too.

Altogether, synthetic user testing is fast, repeatable, and scalable enough to de-risk decisions before they go live and allow UX researchers to focus their efforts on complex problems. It's no wonder that the synthetic data market is projected to grow from $0.4 billion in 2025 to $4.4 billion by 2035.

How to Run Synthetic User Tests with Delve AI

Synthetic testing combines LLMs, persona generation, and browser automation in the backend to simulate real interactions at scale. Synthetic Research Software by Delve AI follows the same process.

It includes three steps in the following order:

1. Persona creation: Create personas enriched with external and internal data.
2. Synthetic user generation: Generate synthetic user panels based on your desired persona(s).
3. Research study: Conduct user testing studies (for live URLs or design flows).
4. First, create personas using our AI Persona Generator. They will act as the base for your synthetic users.

Step 1: Create AI personas

For starters, you can pick our Research Persona tool to generate AI-powered personas from your research data (survey transcripts, user interviews, market reports, etc).

Just log in to Delve AI, visit the Research Persona dashboard, specify your segments, and upload your research files. Besides your audience description, you also get the option to set distribution across factors like gender and location (eg, Gender: Female, 30%).

The tool will then generate user personas for your brand using AI and machine learning systems. For reference, we’ve generated user personas for Nike.

Each persona gives you a 360-degree view of your target audience segment and offers both quantitative and qualitative insights into user demographics (age, gender, location, career profiles), psychographics (goals, needs, motivations, challenges), behaviors (buying triggers, barriers), preferences (channels, social networks, brands), and more.

You can find the complete list of persona attributes here.

Step 2: Generate synthetic users

The second step after persona creation is synthetic user generation. So, after creating your personas, visit the Synthetic Research software and pick Panels from the sidebar menu.

Once there, follow these steps to create synthetic panels:

• Name your synthetic user panel, eg, User Testing Panel.
• Choose the persona(s) you want to use to generate the synthetic panel, i.e, Research Persona > All segments.
• Select the number of users and click “Generate users.”

The synthetic research software will then create synthetic respondents for you in minutes. You can check out their basic demographic details – eg, name, age, marketing generation, location for B2C users, plus job title and company name for B2B users – and also chat with them individually if the inspiration strikes.

Since these users are derived from your personas, which are informed by user data and customer feedback, they can mimic human behavior (i.e., your users and buyers) like no other. This panel can be used to run:

• Surveys + interviews
• Focus group discussions (human or AI-assisted)
• User testing studies

The next subsection will tell you how to conduct synthetic user testing studies in Delve AI; however, if you want to learn more about synthetic surveys, interviews, and focus group discussions, please click the highlighted text.

Step 3: Run a synthetic user test

Before you get started, you need to be sure of what your objectives are for this exercise. For instance, do you want to test site navigation, refine conversion funnels, or improve the whole user experience? To show you how the process works, we’ll create a study to test Delve AI’s “Book a demo” flow.

In the Synthetic Research dashboard, go to Studies and click “Create a new study,” then:

• Select the study type from the drop-down, i.e., User Testing.
• Specify your study name, number of users, panels, and study goal.
• Add the tasks or steps you’d like the users to complete.
• Attach the designs or URLs of the pages you’d like to be tested.

You can also add any post-test questions you’d like to ask your users once they’ve finished the exercise (you’ll get the interview transcripts at the end).

Your synthetic user will connect to real browser environments to interact with actual rendered pages, identify elements, and perform real actions. They apply reasoning and logic to complete task goals just like real users would. Our system captures all this interaction data and displays it on the dashboard after the test is complete.

You can view the Journey by respondents or steps, see the number of actions, and learn about their reasoning at each decision point. You also get screenshots at each touchpoint.

The Report tab includes an executive summary of the user test, which holds key synthetic insights, usability and accessibility feedback, and a heuristic evaluation of the user interface against established usability principles.

You can use this information to adjust the user flow, layout elements, and navigation features. The best part is that you can rerun studies after every iteration and continuously update your synthetic users with fresh data.

Besides usability tests, our synthetic user testing platform can also simulate user journeys, validate layout changes, and test features across different demographics and persona profiles to inform product development.

Some Limitations of Synthetic User Testing

According to a post by the Nielsen Norman Group, “Synthetic users… capture general trends in human behavior but fail at capturing the magnitude of the effects or the variability in the human data.” This is one of the biggest arguments against synthetic testing by UX researchers, marketers, and Redditors.

Emotions, depth, and unpredictability.

Synthetic user behaviors are grounded in anonymized, aggregated data, not in the lived experience of any one individual. Users lack real-life context, are predictable in ways that actual people aren't, and have difficulty representing the actual human condition, emotions, and opinions.

Plus, they don’t feel anger or frustration and operate with logic. Normal people are not logical; they make decisions based on preferences that vary by mood and context. You’ll notice with many synthetic research tools that while quantitative responses have 80-90% accuracy rates, subjective questions often do not match human responses.

Bias, data, and accuracy.

Synthetic users are probability engines usually built on past data, which limits their ability to form new opinions or predict future user behavior. They are highly sensitive to prompt and data quality. Any bias or inaccuracy already present in the training data gets amplified in study results.

Accuracy is also a problem. As per recent studies, AI participants tend to answer questions in the affirmative, always providing positive feedback even when real customers would be more critical.

Ethics and transparency.

Beyond output accuracy, synthetic testing raises ethical questions that can undermine the quality of the research itself. If synthetic data is informing decisions, the people affected deserve to know that; passing it off as data from real users, even unintentionally, is a form of misrepresentation companies need to be wary of.

The Future of Synthetic Research with AI Personas

Right now, synthetic user testing is super useful if you don’t have the time, budget, or in-house expertise for proper usability studies. It can be a starting point to pressure-test concepts before you commit to actual research, but it should definitely not be used to make high-stakes decisions on its own.

After all, synthetic user research is meant to complement genuine human research. It should be an input among many, and not the final decision-making factor.

To keep this balance in practice, always:

• Cross-check synthetic findings against real human research before acting on them.
• Measure the calibration gap: how far predictions land from what people actually do.
• Acknowledge limitations and see whether findings hold up across different tools.

The best way is to adopt a hybrid research approach, wherein you use AI personas to narrow down what's worth validating with real people. Start small, validate your synthetic data rigorously, and be transparent about where it came from.

Ready to start synthetic user testing? Sign up for Delve AI’s Synthetic Research software!