AI Usability Testing: Methods, Tools and Reviews

AI is not just a buzzword anymore, it's a tool that's rapidly changing how we work. In UX research, AI can help researchers gather and analyze data faster than ever before. It can spot patterns that humans might miss. And it can help us test our designs in new ways.

But what does this mean for UX professionals? How can we use AI in our work? And what should we be careful about?

In this article, we'll explore these questions. We'll look at how AI is being used in different types of usability testing. We'll see how AI usability testing can help with both qualitative and quantitative research. And we'll think about what this means for the future of UX design.

Along the way, we'll answer some key questions:

Let’s dive in!

Usability testing involves observing real users as they interact with a product, website, or application. The primary goal is to identify any issues or obstacles that users encounter during their interaction.

In usability testing, participants are given specific tasks to complete. Researchers observe their behavior, listen to their feedback, and measure their performance. This process helps designers and developers understand how users actually interact with their product, rather than how they think users might interact.

Evaluation in usability testing refers to the analysis of the data collected during these sessions. This includes both quantitative metrics (like time on task or error rates) and qualitative feedback (such as user comments or observations of body language).

The benefits of usability testing are numerous:

• It identifies problems early in the design process, saving time and resources.
• It provides objective data about user behavior, reducing reliance on assumptions.
• It helps create products that are more intuitive and user-friendly, increasing user satisfaction and loyalty, leading to better business outcomes.

Usability testing can take various forms, from formal lab studies to more informal "guerrilla" testing. With the advent of AI, new methods of conducting and analyzing usability tests are emerging, offering even more insights into user behavior.

Ultimately, usability testing and evaluation are about putting the user at the center of the design process. By understanding how real people use a product, designers can create experiences that are not just functional, but truly user-centered.

Guerilla Usability Testing

Guerrilla usability testing is a quick, informal method of gathering user feedback. It's called "guerrilla" because it's fast, unconventional, and often done with limited resources. 

How to do guerrilla usability testing

1. Define your goals: Decide what you want to learn. Are you testing a specific feature? Looking for general feedback?

2. Prepare your materials: Have your prototype or product ready. Prepare a short script with tasks for users to complete.

3. Find participants: Go to public places where your target users might be. Coffee shops, libraries, or college campuses are good options. If you’re doing it online, Reddit channels, Facebook groups and Linkedin communities are a few places to start.

4. Approach potential participants: Be friendly and explain what you're doing. Offer an incentive if possible, even if it's just a coffee.
   ‍
   
5. Conduct the test: Ask the participant to complete specific tasks. Observe their behavior and listen to their thoughts.
   ‍
6. Take notes: Record any issues the user encounters, as well as positive feedback.
   
   
7. Analyze and report: Compile your findings and share them with your team!

Guerrilla testing has several advantages—-it’s quick and inexpensive, and a sure-fire way to get real-world feedback, at any stage of product development.

However, it also has limitations. For one, the sample may not be representative of your target users. Also in guerilla user testing, the environment isn't controlled, which can affect results, and you may not get in-depth insights.

Consider guerrilla testing when you need quick feedback or when you're working with limited resources. It's particularly useful for early-stage ideas to test rough prototypes or concepts to guide your design direction. It’s also useful if you're debating between design options, or on a tight budget.

‍Here’s an example of guerrilla usability testing.

Let's say you're developing a new feature for a food delivery app. Here's how you might conduct a guerrilla usability test.
The goal is to test the usability of a new "group order" feature.
So you pick a location where you might find the most likely target audience—let’s say, the food court of a mall on a Friday evening.

You also need a usable prototype of the app in place to test participants with. Also have something to offer people to compensate for their valuable time, like free Amazon vouchers or gift cards.

The participants? Anyone who looks like they might use food delivery apps.

You could simply start with "Hi, I'm working on a food delivery app. Would you mind trying out a new feature for a couple of minutes?"

The agenda for the guerilla user testing can be asking each participant to complete these tasks on the prototype:

• Please start a group order for lunch with your friends.
• Add three items to the order from different restaurants.

Next step, observation. Watch how easily participants complete these tasks. Note any confusion or mistakes.

Follow it up with questions to assess the user experience. Jot down their answers or record voice memos. The questions can include—-How easy or difficult was it to use this feature? What did you like or dislike about it? Would you use this feature in real life? Why or why not?

Once you’ve thanked the participants, it’s time for analysis! Compile the feedback and observations. Look for common issues or positive reactions.

Remember, guerrilla testing is about getting quick, real-world feedback. It's not as rigorous as formal usability testing, but it can provide valuable insights to guide your design process.

Check out our definitive guide to must-have usability testing questions, including an Airtable of popular incentives, and a question bank!

Types of Usability Testing Methods

Usability testing can be both qualitative and quantitative. It often combines both methods to get a comprehensive understanding of user behavior and preferences.

Qualitative usability testing

What is a quantitative usability test?

Quantitative usability testing focuses on measurable data. It answers questions like "how many?" or "how long?" This type of testing is useful for comparing designs or tracking improvements over time.

A quantitative usability test typically involves a larger number of participants and uses metrics such as:

• Task completion rate: The percentage of users who successfully complete a task.
• Time on task: How long it takes users to complete a specific task.
• Error rate: The number of mistakes users make during a task.
• System Usability Scale (SUS): A standardized questionnaire that measures perceived usability.

Quantitative tests are useful for benchmarking against competitors or previous versions, identifying specific problem areas (e.g., tasks with high error rates) and providing concrete data to stakeholders

Qualitative usability testing

Qualitative usability testing focuses on understanding why users behave in certain ways.  It's particularly useful for identifying usability issues and generating ideas for improvements.

‍How to do qualitative usability testing

Here's how to conduct a qualitative usability test:

1. Define your goals: What do you want to learn about user behavior or preferences?
2. Recruit participants: Choose people who represent your target users.
3. Prepare tasks and questions: Create realistic scenarios for users to work through.
4. Conduct the test: Ask users to think aloud as they complete tasks. Observe their behavior and ask follow-up questions.
5. Analyze the results: Look for patterns in user behavior and feedback. Identify common pain points or areas of confusion.

Is qualitative usability testing in-person or remote?

Qualitative usability testing can be done both in-person and remotely. Each method has its advantages.

In-person testing allows for direct observation of body language and facial expressions, and enables easier intervention if users get stuck. It also gives the researcher more control over the testing environment.

Remote testing meanwhile allows access to a wider pool of participants, and can be more cost-effective.

With advances in technology, remote qualitative testing has become increasingly popular and effective. Video conferencing tools and screen sharing make it possible to observe users and gather rich qualitative data from anywhere in the world.

4 types of usability test questions

In usability testing, four types of questions are commonly used:

1. Background questions: These gather information about the user's experience and context. For example: "How often do you use food delivery apps?"

2. Task-based questions: These ask users to complete specific actions. For example: "Please order a pizza for delivery to your home address."
   
   
3. Follow-up questions: These probe deeper into the user's experience after completing a task. For example: "What did you find most challenging about that process?"
   ‍
   
4. Reflection questions: These encourage users to think broadly about their experience. For example: "If you could change one thing about this app, what would it be?"

Common concerns in usability testing

If you’re running usability testing, keep an eye out for these common issues:

• Participant bias: Users may try to please the researcher rather than act naturally.
• Sample size: Determining how many participants are needed for reliable results.
• Test environment: Creating a setting that's realistic yet controlled.
• Task design: Ensuring tasks are relevant and representative of real use.
• Data interpretation: Drawing accurate conclusions from complex user behavior.
• Balancing qualitative and quantitative data: Deciding how to weigh different types of information.

These limitations have led to the development of new testing methods, including the use of AI in usability testing. AI can help address some of these challenges by analyzing large datasets, capturing more subtle user behaviors, and even predicting future user needs.

Can usability testing be automated? TLDR: Yes, but partially.  While AI can't completely replace human insight, it can significantly streamline the process and enhance the depth of analysis.

How to use AI as a UX researcher?

As a UX researcher, you can use AI in several ways:

• Automate Routine Tasks: Use AI to handle tasks like scheduling interviews or transcribing recordings. This frees up your time for more complex work.
• Enhance Data Analysis: Use AI tools to analyze large datasets. This can help you find insights you might miss on your own.
• Improve User Testing: Use AI-powered tools to run more tests, more quickly. AI can help you recruit participants, run tests, and analyze results.
• Generate Ideas: Some AI tools can help with ideation. They can suggest design solutions based on user data and best practices.
• Create Prototypes: AI can help create quick prototypes based on your specifications. This can speed up the testing process.
• Analyze Competitor Products: AI can help you analyze competitor products more quickly and thoroughly.

What is the use of AI in usability testing?

AI brings several benefits to usability testing. It can handle quantitative analysis and basic qualitative insights, but human researchers are still crucial for interpreting complex user behaviors and emotions. Ultimately, AI usability testing works best when combined with human expertise. If you’re looking for speedier ways of running usability tests, you should also check out AI for guerilla usability testing.

How to use AI for quantitative usability testing:

• Sentiment analysis: Use AI to analyze user comments and feedback, identifying positive and negative sentiments.
• Speech-to-text transcription: Automatically transcribe user interviews for easier analysis.
• Emotion recognition: Employ AI to detect user emotions through facial expressions and voice tone during testing sessions.
• Natural Language Processing (NLP): Analyze open-ended responses to identify common themes and concerns.

Here’s few capabilities to help with how to use AI for qualitative usability testing

• Automated metrics tracking: Use AI to measure and analyze key metrics like time-on-task, error rates, and click paths.
• Heatmap generation: Employ AI to create visual representations of user interactions with interfaces.
• A/B testing analysis: Let AI compare different versions of a design to determine which performs better.
• Predictive modeling: Use machine learning to forecast how changes in design might affect user behavior.

How will AI Impact the Future of UX Design & Research?

As AI keeps improving, it will change UX design and research even more. It will help us work faster and find deeper insights. But it won't replace the need for human creativity and empathy in UX work. The future of UX will likely be a partnership between human researchers and AI tools, each bringing their own strengths to the table.

As AI technology continues to advance, we can expect even more sophisticated uses of AI for types of usability testing:

• Predictive user modeling: AI might be able to simulate user behavior, allowing designers to test interfaces before involving real users.
• Adaptive testing: AI could dynamically adjust test scenarios based on user responses, providing more personalized and insightful testing experiences.
• Cross-platform analysis: Advanced AI might seamlessly analyze user behavior across multiple devices and platforms, providing a holistic view of the user experience.
• Automated design suggestions: AI could not only identify usability issues but also propose design solutions based on best practices and historical data.
• Virtual reality (VR) integration: AI could analyze user behavior in VR environments, opening up new possibilities for immersive usability testing.
• Ethical AI for inclusive design: Future AI tools might help ensure that designs are accessible and inclusive for all users, regardless of their abilities or backgrounds.

If you’re looking for a great tool that enables using AI for usability testing, we have 4 options for you.

1. Lookback

Pricing: Starts at $25 monthly
G2 rating: 4.3/5

Lookback is a platform that facilitates remote user research and usability testing. It allows researchers to conduct both moderated and unmoderated studies across various devices. Lookback stands out for its ability to capture user interactions, audio, and video simultaneously, providing a comprehensive view of the user experience.

Here are some of Lookback’s coolest features including using AI for usability testing:

• Automated Task Sequences: Lookback allows researchers to set up step-by-step tasks for participants to follow, with the ability to automatically open URLs at each step to control the test experience.
• Secure and Account-less Participation: Lookback provides a seamless and secure experience for participants, without requiring them to create accounts or enter credentials.
• Cross-Device Testing: Lookback supports usability testing on desktop, iOS, and Android devices, giving researchers the flexibility to test across platforms.
• Participant Management Integration: Lookback integrates with User Interviews to enable powerful automation and management of research participants.

Lookback’s Pros:

• Free trial available for testing
• Timestamping and note-taking features for quick reference
• Observer room for team members to view sessions without participant knowledge
• Effective for mobile usability testing

Lookback’s Cons:

• Some users report difficulties with unmoderated research tasks
• Higher participant no-show rates due to unfamiliarity with the platform
• Doesn't solve for participant recruitment

2. Maze

Pricing: Free plan available, paid plans start at $99 monthly
G2 rating: 4.5/5

Maze is a rapid testing platform that allows teams to test everything from prototypes to live websites. It offers a variety of testing methods including usability tests, preference tests, and card sorting. Maze is also known for its integration with popular design tools and its ability to generate automated reports.

Here are some of Maze’s AI usability testing features:

• Dynamic Follow-Up: Maze's AI can automatically generate follow-up questions based on user responses, allowing for more contextual and insightful feedback.
• Contextual Suggestions: The AI provides real-time suggestions to researchers on how to improve their tests, such as adding more relevant tasks or questions.
• Bias Detection: Maze's AI can detect potential biases in test design and participant selection, helping researchers create more objective and representative studies.
• Automated Analytics: Maze leverages AI to generate visually-rich, customizable reports that summarize key usability metrics like success rates, task completion times, and user satisfaction.

Maze Pros:

• Easy integration with design tools like Figma
• Over 200 pre-made templates for quick test setup
• Combines quantitative and qualitative data collection
• User-friendly interface for building tests
• Automated report generation

Maze Cons:

• Learning curve for new users
• Prototype crashes has been reported by some users, especially on mobile devices
• Limited email campaign management
• Limited export options for presentations

3. UserTesting

‍

Pricing: Contact their team for custom pricing quote
G2 rating: 4.5/5

UserTesting is a comprehensive platform for gathering customer insights. It offers a wide range of testing options, from unmoderated tests to live interviews. UserTesting is known for its large panel of testers and its ability to target specific demographics.

UserTesting’s AI capabilities for usability testing include:

• AI Insight Summary: Summarizes key learnings and important moments from video, text, and behavioral data.
• AI Survey Themes: Automatically generates key themes from high volumes of open-ended survey responses.
• Friction Detection: Identifies and analyzes friction points that users encounter during their interactions with digital products.
• Sentiment Analysis: Surfaces moments of negative and positive sentiment from user research sessions recordings.

UserTesting Pros:

• Large, diverse panel of testers
• Comprehensive solution for various types of user research
• Powerful analytics and reporting features
• Flexible test creation options
• Integration possible with other tools in the research workflow

UserTesting Cons:

• Can be expensive for smaller teams or organizations
• Learning curve for utilizing all features effectively
• Some users report occasional issues with participant quality
• Complex pricing structure

4. Odaptos

‍

Pricing: Free plan available, paid plans start at $300 monthly

Odaptos is an AI-powered platform designed to enhance user experience research. It uses advanced technologies like facial detection and natural language processing to provide deep insights into user behavior and emotions during testing sessions.

Here are some of Odaptos’s coolest features including using AI for usability testing:

• Emotion Recognition: Uses facial detection to analyze user emotions during testing sessions.
• Natural Language Processing: Analyzes user comments and feedback to extract meaningful insights.
• Automatic Transcription: Converts speech to text for easier analysis of user feedback.
• Sentiment Analysis: Determines the overall sentiment of user responses.
• System Usability Scoring: Provides automated scoring based on user interactions and responses.

Odaptos Pros:

• Accelerates user testing process with AI-powered analysis
• Provides emotion-based insights for deeper understanding of user experience
• Offers a collaborative platform for team access to results
• Flexible pricing based on number of interviews needed
• Comprehensive feature set including transcription and usability scoring

Odaptos Cons:

• Potential privacy concerns due to extensive use of facial detection
• Learning curve for teams to fully leverage the platform's capabilities
• Heavy reliance on AI may not suit teams preferring more manual approaches
• Limited user panels in some pricing tiers 

While the terms "user testing" and "usability testing" are often used interchangeably, there are subtle differences between the two:

User testing focuses on overall user experience and satisfaction, and can cover a broader range of topics, including user preferences and behavior. It may involve more open-ended tasks and questions, and often includes both qualitative and quantitative data. User testing can be conducted at various stages of product development.

Usability testing specifically focuses on ease of use and task completion. It typically involves specific, measurable tasks. Usability testing is often more structured and controlled, and primarily concerned with efficiency, effectiveness, and user satisfaction in completing tasks. It’s usually conducted on a near-final or live product

Despite these differences, both methods are crucial for creating user-centered designs and are often used in conjunction with each other.

Also read: The difference between Concept Testing and Usability Testing

FAQs

Can I use AI to analyze qualitative data?

Yes, AI can help analyze qualitative data. It can help you find themes in open-ended survey responses, analyze sentiment in user feedback, as well as transcribe and analyze interview recordings!

Find the complete guide on How to use ChatGPT for Usability Testing here.

Is usability testing an example for qualitative assessment method in AI application?

Usability testing can be both qualitative and quantitative. When we use AI in usability testing, it often involves quantitative methods. AI can track metrics like time on task or error rates very precisely.

But AI can also help with qualitative aspects of usability testing. It can analyze user comments, facial expressions, or tone of voice. This gives us qualitative insights into the user's experience.

So, usability testing with AI can be both a qualitative and quantitative method. It depends on how we use the AI and what data we're looking at.

Can AI do user testing?

AI can certainly play a significant role in user testing, but it cannot completely replace human involvement. AI can enhance user testing in several ways like automating data collection, identifying trends and patterns in human behavior, sentiment analysis, predictive modeling on user behavior, and large scale data analysis and testing.

Can I do user testing on Reddit?

Reddit can be a valuable resource for user testing and gathering feedback! It has a large, diverse user base, and you can target niche audiences through subreddits specifically. 

User testing Reddit

Reddit users are known for providing candid, sometimes brutally honest opinions, so good feedback is guaranteed. Posting on Reddit is free, making it an economical option for gathering initial feedback.

However, using Reddit for user testing also has challenges like the potential for bias, moderation issues and lack of control in building a representative sample, or managing the testing environment.

While Reddit can be a useful tool in the user testing toolkit, it's best used in conjunction with more formal testing methods for comprehensive results.

Checkout this detailed guide on UX Research Methods