Illusions of AI Usability Testing, and How to Make It Work In Your Favor

User Experience, Usability, & User Interface—A Brief Refresh

First, we need to understand why AI is being pushed into usability testing. Is it another gimmick or an actual revolution in how we do things? Let’s start with getting to know usability and its relatives.

What Is Usability In Software?

Usability focuses on how an actual person uses your product. It includes the technical metrics, such as ease of navigation, and, to a degree, psychological aspects, such as the emotional response. So, let’s say you’ve just downloaded a brand-new game. Your first hours will be spent on figuring out the kinks—what to do and how to progress.

During this “orientation” period, you’d also be discovering how you feel about the game.

• Can you grasp its mechanics quickly, or do you need to find a tutorial?
• Are you having fun while playing?
• If something goes wrong, can you solve the issue on your own?

In general, non-game context, usability is basically about people doing what they need to do without running into obstacles. Take a look at the core criteria of usability:

1. Learnability. How fast can a person figure out what they need to do to achieve their goal?

Example: a novel user opens Google Docs and doesn’t struggle to understand how to create, work, and format a document.

2. Efficiency. How quickly can a person do what they need to do?

Example: in Slack, sending a message, sharing a file, or reacting with an emoji can be done in just a few clicks.

3. Memorability. When a person returns to your app after a break, can they remember how to work with it easily?

Example: a user who hasn’t used Airbnb for months logs in and still easily books a stay because the process is consistent and familiar.

4. Error management. How many mistakes a person makes, and how gracefully can they recover?

Example: Gmail warns you if you forget to attach a file after writing “I’ve attached…” in your email. This helps avoid common errors.

5. Satisfaction. How happy is a person using the app the way it’s intended?

Example: Spotify’s clean layout, responsive controls, and smart recommendations make users feel in control and enjoy the experience.

So, usability has to do with the actions a person takes.

What Is User Experience in Software?

User experience (UX) is about what a person experiences while performing those actions. Let’s consider a scenario where you open an e-commerce page and start ordering what you want.

• The site has a nice design and feels familiar.
• You can add the items to the cart.
• The checkout button is clearly visible and working.
• You can complete the payment.

Overall, you’ve performed the action you wanted by ordering a product. But during this, you notice the following:

• The site doesn’t show shipping costs until the very last step.
• The progress bar disappears halfway through.
• The color contrast makes it hard to read on mobile.
• You get a generic confirmation message like: “Transaction successful”. There’s no option to get a receipt.

The above is an example of an app with good usability but poor UX. You did what you wanted to. But you didn’t exactly enjoy it because of all the little annoying things.

UX also has its defining aspects.

1. Usefulness. Does the app solve a real problem or meet a real need?

Example: An app tracks how many cups of coffee you drink per day. But it offers no insights, reminders, or health tips.

2. Usability. Is the app easy to use and navigate?

Example: A budget tracking app where the “Add Expense” button is tiny, hidden in a menu, and takes five steps to use.

3. Desirability. Does the app look and feel good to use?

Example: Two apps do the same thing. But one has playful icons, smooth animations, and a friendly tone. The other is plain and stiff. Of course, people will prefer the first one.

4. Findability. Can users easily find what they need?

Example: A recipe app with no clear categories, poor search, and no filters.

5. Accessibility. Can everyone use the app, including people with disabilities?

Example: A news app uses light gray text on a white background and can’t be used with a screen reader.

6. Credibility. Does the app feel trustworthy and professional?

Example: A finance app that looks outdated, has typos, and no clear privacy policy. It works, but users won’t trust it with their data.

7. Value. Does the app deliver a benefit that feels worth the effort or cost?

Example: A health app that tracks every possible stat but overwhelms users with jargon and charts. It may be powerful. But if it doesn’t feel worth learning or paying for, the perceived value drops.

So, usability is doing what you need to do. UX is enjoying what you’re doing.

What Is User Interface in Software?

User interface is very straightforward but a little tricky. When we think UI, we think design, structure, and beauty. But there’s a functional aspect to it. You can have a stunning webpage, yet if it doesn’t work properly, visual magnificence stops to matter. Briefly, UI is about the balance between being pretty and operational.

It covers the following:

• Visual design elements (colors, typography, icons, images).
• Layout and composition.
• Interactive elements (buttons, forms, navigation).
• Responsiveness.
• Visual hierarchy and info presentation.
• Animation and transitions.
• Design consistency and patterns.

So, to sum everything up:

• UI is about what a user sees and interacts with.
• Usability is about how easily and efficiently a user can do what they need.
• And UX is about the all-encompassing experience a user has with your product.

Now, let’s get back to the start of the show—usability testing with AI.

The Hows of Using AI for Usability Testing

When it comes to AI in QA automation (or AI in anything, for that matter), there’s one thing people may overlook—the huge gap between expectations and reality. We sometimes hear the famous “artificial intelligence is capable of thought, creativity, and innovation”. So, some immediately assume that AI usability testing tools eliminate the need for human labor. What they actually do is simplify it.

What Is AI Usability Testing?

Even the best AI automation testing tools have their limitations. They don’t handle context well, they don’t understand user intent and nuance, and they don’t see logic behind data. AI tools for usability testing will tell you when there’s an issue. Yet, they won’t explain why it’s there.

• For example, a spike in drop-offs might be flagged. But AI won’t understand that confusing terminology or emotional frustration caused it.
• It may misinterpret neutral behaviors as problems (or vice versa).
• If the AI model is trained on limited or non-representative user data, it can produce misleading conclusions.
• Plus, it lacks empathy and human judgment to fully assess qualitative aspects.

So, when explaining how to use AI for usability testing, we must be objective. Here’s what artificial intelligence can do really well.

• Automating routine tasks. AI can automatically tag user sessions, detect common behaviors (like rage clicks), or flag repeated navigation loops.
• Data processing & analysis. AI quickly processes large volumes of user interaction data to identify patterns and potential friction points.
• Generating ideas for tests. AI tools can suggest usability test scenarios based on user data, feature changes, or known design heuristics.
• Analyzing outputs. AI helps turn raw data into summaries, visualizations, and actionable reports by grouping issues, highlighting severity, and suggesting fixes.
• Analyzing competitor products. AI can scan and compare competitor interfaces, reviewing layout structures, user flows, or accessibility features to identify design gaps or best practices.
• User feedback analysis. Using natural language processing, AI can sift through open-ended survey responses, reviews, and support logs to extract usability concerns and sentiment.
• Predictive modeling. AI can simulate user behavior on new designs or forecast how changes may impact user flow.
• Accessibility checks. AI can detect fundamental accessibility issues that impact usability.
• UX benchmarking. AI can score a design based on usability heuristics or compare its structure to top-performing apps in similar domains.
• Personalization testing. AI can help test how different user segments respond to personalized interfaces and identify which designs work best for whom.

What can we gather from the above? That’s right, AI is incredible when it comes to working with data. But sophisticated or advanced tasks should be off-limits (for now). It’s very similar to the use of AI in UI testing services. It will definitely recognize a faulty or missing button. But it won’t tell you if an app is visually appealing overall or fits your brand.

How to Use AI for Types of Usability Testing?

Because of artificial intelligence’s nature, using AI for usability testing methods should also be approached carefully. That’s why here, we’ll go over testing types that benefit most from the digital mind.

Unmoderated Testing

AI is most effective here. It can analyze large volumes of screen recordings, detect rage clicks, identify drop-off points, and cluster usability issues across users. It speeds up reporting but can’t explain why users behaved a certain way.

Task-Based Testing

AI can track completion rates, time taken, and misclicks across tasks. It can also highlight recurring failure points and suggest possible UI bottlenecks. It’s great for speed and scale, but still relies on humans to assess the clarity and intent of tasks.

Comparative (A/B) Testing

AI is strong at processing behavioral data from two or more versions of a design. It can measure task success, time-on-task, or frustration signals. Though subjective preferences still need human judgment.

Eye-Tracking Studies

AI powers many eye-tracking tools, helping visualize gaze patterns, heatmaps, and attention zones. It processes large visual datasets quickly. But interpreting why users looked where they did still requires a UX expert.

Longitudinal Testing

AI can track user behavior over time, flag changes in patterns, and predict usability degradation. It’s useful for surfacing slow-building friction. But human review is needed to validate long-term impact.

As you can see, using AI for types of usability testing means mostly working with quantitative aspects. Don’t get us wrong. AI testing services pair superbly with qualitative research as well. You can create synthetic personas, transcribe user sessions, cluster and tag feedback, etc. Assigning AI to such tasks has two huge benefits—speed and focus.

AI can comb through tons of data while you take a five-minute break. And when you come back, you already have neat and useful info to continue your work. So you have more time for doing what brings in meaningful insights. We do encourage you to use AI for quantitative usability testing. But do remember that statistics aren’t everything.

How to Work with the Limitations of AI Usability Testing

Now that we know that AI is useful but far from perfect, let’s figure out how to work around its constraints. Long story short, you need human experience and great skills. Whether you’re working with an in-house team or QA outsourcing services—make sure you have actual specialists on your side. Because, frankly, no matter how groundbreaking an AI tool for usability testing may be, it can never replace the value of an expert’s insights.

Overrelying on Quantitative Metrics

Using AI for usability testing can tempt you to not go further. For example, let’s say a tool noticed that a user stopped doing anything right as they were about to press the “Pay” button on an e-commerce app. AI flags it as an issue. And the first assumption would be that there’s a problem with the button itself (given that everything else seems okay).

Going off of AI’s output, you go to investigate the problem. But then you realize that the button is working just fine. There’s no actual error. There are two possible scenarios after that:

• Assuming that a user got tired, just fell off, etc. (there’s no actual issue with the app).
• Investigating the user journey, requesting feedback, and asking what-if questions if necessary.

If you’re going with option number one—you’re gambling. If you’re using option number two, you’re getting to the bottom of the issue, locating the real problem, and fixing it. Continuing with our little fake scenario, in the end, you find out that the user was accumulating frustration and simply rage-quit at the end. Why? The color contrast on your page was uncomfortable for the user. They were struggling to see all that time and decided to finally leave.

Balancing AI Insights & Human Expertise

Given the above, it’s important to supplement AI deficiencies with human skills. The issue is finding a balance between the two. If you’re second-guessing every AI move, you’re wasting time. If you’re not using it at all, you’re missing out.

The first thing you need to do is proper research on AI usability testing tools. Pick the one that fits your needs and niche. Then, make sure you have a specialist on your team with AI proficiency who knows your tool well. After all, you’ll need to set up, run, and maintain it.

Use AI usability testing for straightforward tasks with no room for speculation, such as fundamental accessibility checks, and data processing, such as feedback analysis. Rely on your team when human judgment is essential, such as during moderated usability sessions.

Remember that you may approach such a balance differently. Every project is unique, after all. But if you’re just figuring stuff out, we recommend using AI for data and letting people do the rest.

Securing Privacy & Ethics in AI Usability Testing

This issue is very simple but might be overlooked. To avoid worry and potential legal troubles, make sure your AI tool has clear policies on:

• How data is anonymized.
• How info is stored\deleted.
• How unethical practices, like over-collection of behavioral data, are avoided, etc.

And keep in mind that AI usability testing tools that don’t disclose such info just might dabble in other bad stuff. Manipulating insights or simply lying to make the provider look good—these are always a possibility.

Ensuring Diversity & Inclusivity

“You are what you eat” is very fitting when it comes to AI. Because it feeds on data. And if that data is of poor quality, you can’t really trust your usability testing tool. So, here’s what you need to do:

• Build diverse datasets to make the AI well-adjusted. Include edge cases, too.
• Tag and annotate demographic data so that AI respects behavior differences instead of flagging, say, an older person as a “problematic user”.
• Audit AI for behavioral bias (find out more about this in our article on how to test AI applications).

Most importantly, include AI specialists, UX researchers, etc., when reviewing AI-generated insights.

Including Cultural & Contextual Nuances

AI tools for usability testing might not offer the most precise insights when it comes to subtleties, context, or cultural nuances. Frankly, they might just miss them. Even if you work with the best AI model there is, mistakes or false positives can still appear. There are only two ways to mitigate this. Keep refining your tool with high-quality data that’s relevant to different user groups. And rely on people when it comes to complex stuff, such as validating appropriateness.

Guaranteeing Quality Results with AI Usability Testing

This section concerns two points:

• Making sure your AI tool does what you need it to, i.e., having experts who can work with it.
• And handling aspects that AI can’t cover, i.e., having experts who can take care of tasks AI can’t cope with.

Yes, you got it. You need skilled people. People who can maintain your tool and make sure it actually does what it should. And people who can successfully work with complex usability aspects.

Keep in mind that usability testing can be conducted during different phases of development. If you’re working with it at earlier stages, you can rely on QA engineers to cover the fundamentals. For example, QA Madness’ experts can run basic usability checks as they’re working on other aspects of your product. This way, you can secure core usability practices early on. And further checks will have a solid base and go quicker.

To Sum Up

Let’s sum up the key takeaways from this article.

• You should use AI for usability testing for better efficiency.
• And you should remember what it requires from you.

AI models don’t work on their own. Plus, there are many things they’re not yet equipped to do. So, when it comes to the decision on whether to include AI in usability testing at all, remember that it’s really about whether you have what AI needs to function in your favor.