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It's no secret that AI is having an enormous impact on tech, including the field of UX Research. According to a recent survey by User Interviews, 51% of UX researchers are already using AI tools for user research, and 91% are open to using them in the future.
Given the buzz around AI and User Research, there are 3 big questions that typically come up for people:
- Will AI replace UX Researchers?
- Which AI tools should I use to speed up my UX Research?
- How do I run User Research for new AI features or products?
If you're looking for answers to the first two questions, keep reading! If you're trying to figure out how to run User Research for a new AI feature or product, we've put together a comprehensive 7-step guide here.
In this article we'll cover:
Not even close. While AI can be a powerful assistant, it lacks the human intuition, empathy, and domain expertise needed to truly understand user needs. AI User Research tools may point out what is statistically significant, but deciphering the "why" behind user behavior requires human interpretation. UX research is both art and science.
That doesn’t mean you should ignore AI though. UX Research and AI can be incredibly synergistic, if used correctly. Instead of letting it do all the work, think of AI as your trusty sidekick - it can handle the grunt work and crunch the numbers, freeing you up to focus on the strategic, creative aspects of UX research. By leveraging AI UX research tools, you can work smarter, not harder, and deliver even more value to your organization.
2022 saw an explosion of generative AI tools like ChatGPT and Dall-E. These powerful language and image models can understand complex prompts, answer questions, and even generate original content. While the technology is still developing, it has massive potential to streamline parts of the UX research process.
There are 6 different types of AI UX research tools that have emerged and are now being used to make UX Research more efficient:
- Transcription & Analysis Tools: Say goodbye to manual transcription! AI User Research tools like Looppanel can transcribe your interviews with over 95% accuracy and even automatically tag them for you. You can now skip the manual, tedious analysis steps—no more cleaning transcripts and copy-pasting notes to Miro.
- AI Users: This one’s controversial, but given all the hubbub around them, they deserve a mention. Some tools are replacing human users with AI or synthetic users. Is it worth investing in? Jury’s still out, but we have some opinions - keep reading for those!
- Unmoderated Testing Tools: Need to run unmoderated usability tests or in-product surveys? Tools like Maze and Sprig run the studies for you and use AI to analyze user interactions, generate heatmaps, and detect sentiments.
- Writing & Ideation Tools: A crucial step in research is communicating your insights. If you’re struggling with communicating your insights, AI-powered writing tools like Copy.ai can help you generate engaging copy and re-phrase key insights.
- Research Repository & Insight Tools: Old-school repositories require manual tagging and complex taxonomies to make data searchable. New-age AI-powered repositories have Google-like search built in to help you find the answers you need in seconds, taking the burden of knowledge management off your team.
- Qualitative x Quantitative Tools: A very new kid on the block—tools that use AI to “moderate” interviews with many users at the same time. These tools try to blend the scale of quantitative data with the depth of qualitative data. Like with some others on this list, the jury’s still out on this one!
Looking for an AI UX Research tool for faster analysis? Check out Looppanel for Free
10 Best AI UX Research Tools
Ready to start harnessing the power of AI in your own research practice?
Here are top 10 AI tools for user research to explore, from full-service research platforms to handy utilities for specific use cases:
1. Looppanel
Key AI Features:
- 90%+ accurate transcripts, ready in 3-5 minutes
- Automatic notes, organized by your research questions
- Auto-tagging data
- Smart repository search to find answers in seconds
How to Use Looppanel AI for User Research: Looppanel is an AI-powered analysis and repository solution built by User Researchers for User Research. 80% of traditional repositories fail because they slow user research down and require a lot of manual maintenance. The extra work means teams eventually abandon repositories and go back to excel sheets and Miro boards.
Looppanel is built to automate the manual, tedious parts of a researcher's workflow so they can:
- Analyze data 10x faster
- Query user data for insights in seconds
Request a Free walk-through of Looppanel
Pricing: Starts at $27 / month
2. ChatGPT
Key AI Features:
- Answer questions from large amounts of data in minutes
- Ability to re-write content
- Ability to provide ideas
How to Use ChatGPT AI for User Research: While not designed specifically for user research, ChatGPT can be a valuable tool in your UX research AI toolkit. Use it to brainstorm research questions, generate survey or interview prompts, and roleplay user personas. You can also leverage ChatGPT to analyze open-ended survey responses, identifying common themes and synthesizing insights. ChatGPT can help you ideate solutions and generate user stories based on research findings.
Here’s a detailed list of prompts you can start playing with!
Pricing: Get access to GPT 3.5 for free, but a much better model (GPT 4!) is available for $20 / month
3. Maze
Key AI Features:
- Automatic analysis of unmoderated tests
- Auto-generated report of findings
How to Use Maze AI for User Research: Maze's AI capabilities make it a powerful tool for unmoderated testing. Set up your prototype or website in Maze, define your tasks, and let the AI analyze how users interact with your interface. Maze's AI generates heatmaps, identifies interaction patterns, and detects usability issues. The sentiment analysis feature helps you quickly gauge user emotions and reactions. Leverage Maze's automated reporting to get actionable insights and share findings with your team.
Pricing: Start for Free! Paid plans from $99 / month
One personal note: their pricing has really gone up recently, and recruiting respondents via Maze in particular is $$$. Maze is also testing some AI features that let it “ask follow up questions” to users smartly. Jury is still out on how good the feature is, but it sure sounds cool!
4. Sprig
Key AI Features:
- Automatic analysis of open-ended survey responses
- Sentiment and emotion detection
- Keyword and topic extraction
How to Use Sprig AI for User Research: Spring is a micro-survey product that can ask your users for feedback in-app. The product uses AI to analyze responses to your surveys,, also detecting sentiments, emotions, and keywords.
Pricing: Start for Free! Paid plans from $175 / month
5. Notion AI
Key AI Features:
- AI writing assistance for research documents
- AI-powered summarization and analysis
- Intelligent search and organization
How to Use Notion AI for User Research: Notion AI can streamline your research documentation and analysis within Notion. Use the AI writing assistant to draft research plans, discussion guides, and survey questions. Notion AI can also summarize and analyze research notes, pulling out key insights.
Pricing: From $18 / user / month (Notion with AI features)
Want to learn how you can use AI for qualitative data analysis? Read this guide.
6. Userdoc
Key AI Features:
- AI-generated user stories and documentation
How to Use UserDoc AI for User Research: More for project scoping, UserDoc is still a handy tool for generating user personas, automatically scoping features, and writing user stories quickly.
Pricing: From $12 / month
7. Synthetic Users
Key AI Features:
- AI-generated user profiles and personas
- Simulated user behavior and interactions
- Scalable user testing and feedback
How to Use for User Research: Synthetic users let’s you test your product with “AI users” (aka not real people) to test and validate designs or gather feedback. . It’s is exactly what it sounds like—it tries to replicate what your users would actually say or do, with AI. The idea is that you can scale your user testing by gathering feedback from a large number of synthetic users.
While we all hate recruiting, I’m a bit skeptical on this one. It’s a hard one not to mention given all the talk around it (it’s the kind of tool that raises the question, “will ux research be replaced by AI?”)
Our view is that there’s a reason we have to keep talking to real people: attitudes, use cases keep changing—and frankly people surprise you. There’s a huge human element in user research—and it’s the user.
Pricing: From $99 / month
8. Miro AI and FigJam AI
Key AI Features:
- AI summarization of stickies
- AI clustering of data
How to Use Miro and FigJam AI for User Research: Miro and FigJam have released AI features to summarize stickies and cluster them by theme. We’ve tried them out—they’re okay at this. Not amazing, but can be a helpful starting point.
If you really want to auto-tag your data, we’ve invested in developing a really good auto-tagging feature on Looppanel. Request a Free demo here!
Pricing (Miro): Start for free! Paid plans from $10 / month
Pricing (FigJam): Start for free! Paid plans from $3 / month
9. Perplexity.ai
Key AI Features:
- AI-powered search
How to Use Perplexity AI for User Research: Perplexity.ai can be a powerful tool for research discovery and context-gathering—it’s like Google with ChatGPT on top. Use it to quickly find relevant information, studies, and data related to your research topic. The really great thing is that it also provides source citations for its claims! This makes it easy to trace information back to its origins and assess credibility.
Pricing: Currently free! It also offers a Pro plan priced at $20 per month.
10. Copy.ai
Key AI Features:
- AI-powered copywriting and ideation
- Customizable tone and style
How to Use Copy AI for User Research: While primarily a copywriting tool, Copy.ai can also assist with various research-related writing tasks. Use it to generate engaging survey questions, participant recruitment emails, or content for your research reports. You can also customize the tone and style to match your target audience. If you need to spin up a report or executive summary in a hurry, Copy.ai is your friend!
Pricing: Start for free! Paid plans from $49/month
Curious about more use cases for UX Design and Research? Read our primer on AI and UX here.
How to Choose an AI Tool for User Research
With new AI tools for UX research cropping up everyday, it can be challenging to select the right one for your needs. Here are some key factors to consider when choosing an AI tool for your user research:
- Research Method: Are you running tons of unmoderated studies, or generative interviews? Do you Choose your AI User Research tool based on the type of research you are (and plan to be) doing.
- Experience level with Prompting: Technically you could buy GPT4 access and use it to do almost anything. If you truly know how to use prompting to gain efficiency in your workflow, go ahead and do this. If you don’t have time to try 50 different prompts, chunk your data into smaller parts, ensure security measure are being met—just choose an AI tool purpose built for user research.
- Integration with Existing Workflow: Evaluate the AI tool's ability to integrate with your existing research workflow and collaborate with other team members. For example, if you’re spending hours manually copy-pasting data into ChatGPT, then copy-pasting its response into a Miro board, is it really saving you time? More importantly, are you about to pull your hair out? Make sure that the AI tool you use aligns with your UX research workflow to avoid spending all the time you saved copying data from one place to another.
- Cost and ROI: Consider the cost of the AI tool and assess its potential return on investment (ROI) for your UX research efforts. Evaluate pricing plans, licensing options, and any additional costs associated with usage, storage, or support.
- Security and Privacy: Prioritize the security and privacy features of the AI tool, especially when dealing with sensitive user data. Assess the tool's data protection measures, compliance with relevant regulations (e.g., GDPR), and the vendor's commitment to data security. If using ChatGPT (or equivalent) make sure you’ve ensured that your data will not be used for training their models.
UX research AI is taking the world by storm, promising to revolutionize the way we gather and analyze user insights. But what do researchers really think about this new frontier? We've talked to UX professionals in the trenches to get their take on the benefits and challenges of using AI in their research practice.
Benefits: Efficiency, Scale, and Depth
Many researchers are excited about the potential of AI to streamline their workflows and uncover deeper insights. As one researcher put it, "AI has been a game-changer for us. We can analyze more data in less time, and the insights we're getting are much richer." Some key benefits researchers are experiencing include:
- Faster data analysis: AI-powered tools can quickly transcribe interviews, identify themes, and summarize findings, saving researchers hours of manual work.
- Increased scale: With AI, researchers can collect and analyze data from a larger number of participants, leading to more representative insights.
- Deeper insights: AI can spot patterns and connections that humans might miss, uncovering hidden gems in the data.
Challenges: Risks and Responsible AI Usage
While UX research AI opens up exciting possibilities, it's crucial to be aware of the risks and limitations. Some key considerations:
- Data privacy & security: When using AI tools, be vigilant about protecting sensitive user data. Look for tools that offer robust security and compliance features. Specifically you want to make sure that any tools you use do not use your data for training purposes. If there’s one thing you take away from this article, let it be this!
- Bias & fairness: AI models can reflect the biases present in their training data, which may lead to skewed or discriminatory outputs. Critically evaluate any AI-generated insights and strive to use diverse, representative data. This is especially true if you’re trying out synthetic users, but you want to review AI outputs for analysis and other use cases as well.
- Transparency & human oversight: Avoid over-reliance on AI by maintaining human oversight and interpretation. Be transparent with stakeholders about how you're using AI and its limitations.
- As you integrate AI into your UX research practice, stay informed on the evolving best practices for responsible AI development and usage. Resources like the IEEE's Ethically Aligned Design provide helpful frameworks.
Best Practices for AI in UX Research
Based on their experiences, researchers recommend the following best practices for AI-powered UX research:
- Start small: Begin by using AI for specific, well-defined tasks rather than trying to overhaul your entire research process at once.
- Combine AI with human expertise: Use AI to augment human skills, not replace them. Human researchers should always be involved in interpreting and validating AI-generated insights.
- Continuously monitor and adjust: Regularly assess the performance and outputs of your AI tools to ensure they are meeting your research needs and ethical standards.
AI is evolving at the speed of light. While no one knows exactly where it will go, our prediction is that it will become a really powerful Research Assistant—transcribing, taking notes, tagging data, and overall helping you discover insights 10x faster.
We don’t think AI will be replacing people in the research process because at the end of the day, generating insights and understanding how they apply to your business is a subjective, human process.
But that doesn’t mean AI can’t help you distill large amounts of data generated by research quickly and efficiently.
Given the speed of change, it's important for UX researchers to stay informed about the latest tools, techniques, and best practices. Do NOT make the mistake of ignoring AI because you’re afraid or skeptical of it.
Make sure you keep your finger on the pulse of AI:
- Experiment with AI tools: The best way to understand AI's potential (and limitations) for UX research is to get hands-on. Try out different tools, from general-purpose assistants like ChatGPT to research-specific platforms like Looppanel. Keep an open mind and think creatively about how AI could fit into your workflow.
- Follow AI updates: Pay attention to new releases, feature updates, and capability improvements from AI tool providers. Many share product roadmaps and release notes that can give you a sense of where the technology is headed. Setting up Google alerts for key AI tools and companies can help you stay in the loop.
- Tap into the AI community: Many researchers and practitioners are exploring AI's implications for UX. Follow and engage with these thought leaders on social platforms to learn from their experiences and insights. A few notable voices to check out:some textsome text
- Cory Lebson (LinkedIn) - UX consultant and author who covers UX topics in general, but often talks about AI's impact on UX careers and practices
- Kritika Oberoi (LinkedIn) - Founder of Looppanel who shares UX resources, including information specific to AI and UX
- Jared Spool (LinkedIn, Twitter) - UX design leader who shares perspectives on AI, chatbots, and the future of UX
- Joe Natoli (LinkedIn) - UX consultant and instructor who covers topics like AI, chatbots, and voice UX
- Attend AI-focused events: Look out for conferences, webinars, and workshops that explore AI's applications in UX research. Many UX and market research organizations are incorporating AI-related content into their events. Some to bookmark:some textsome text
As you immerse yourself in the world of AI, remember that it's an ongoing learning journey. The key is to stay curious, critically-minded, and committed to using AI in ways that positively impact your research and your users. The future of UX research AI is exciting - and a little bit of AI might just make it more human.
Frequently Asked Questions (FAQ)
Is UX Research in High Demand?
Historically UX Research (until 2022) demand for UX Research was rising sharply. In the recent few years as the tech market has tanked, so has demand for UX Research roles. This doesn’t mean everything is doom and gloom—in the long run industry should be on the rise, but in the short run it may be hard to crack into.
Is an AI tool safest to use for Experienced UX Professionals?
While AI UX Research tools can be incredibly powerful, they are not a replacement for human expertise. Experienced UX researchers are best equipped to leverage AI effectively and responsibly. They have the domain knowledge and critical thinking skills to interpret AI-generated insights, spot potential biases or errors, and make informed decisions based on the data.
That being said, AI can also be a valuable learning tool for novice researchers. By working with AI tools under the guidance of more experienced colleagues, OR with tools like Looppanel which put guardrails in for AI, junior researchers can develop their skills and gain hands-on experience with cutting-edge research techniques. The key is to approach AI as a supportive tool rather than a crutch.
Do You Need to Know How AI Works to be able to use AI UX Research tools effectively?
As a UX researcher, you don't need to be an AI expert to use AI tools effectively. Most AI-powered research tools are designed to be user-friendly and require no coding or technical expertise. However, having a basic understanding of how AI works can help you use these tools more effectively and troubleshoot any issues that may arise.
If you’re using a general purpose AI tool like ChatGPT or Perplexity, understanding AI’s limitations and risks is more important. For example, you need to know how to create a useful prompt, how to iterate on your prompts, and how to ensure your data is secure. If you’re using an AI tool purpose-built for user research, many of these challenges should already be taken care of.
How to use AI for UX research methods?
AI can be applied to different UX research methods to streamline data collection, analysis, and insights generation. Here’s how AI can be used for the most popular UX research methods:
- Moderated research: AI can be used to generate your interview guide, transcribe and analyze data. In some cases, folks are also using AI to “moderate” user interviews at scale.
- Unmoderated research: AI can analyze video, audio, and text data to highlight key findings and generate reports.
- Surveys: AI can be used to both create your survey and analyze it (especially open-ended responses).
- All research methods: AI can be used for generating research plans and writing final reports.
How to use AI for creating a UX research plan?
A UX research plan is the first document you’ll create when running a research study. AI can help you create the first draft so you’re not starting from scratch.
If you want to work off of a UX Research Plan template instead, check this article out.
Can AI do UX design?
While AI can assist and augment UX design processes, it cannot fully replace human designers. AI can generate design suggestions, automate repetitive tasks like creating basic frames, and generate digital versions of hand-drawn designs. . However, UX design requires empathy, creativity, and a deep understanding of user needs, which AI currently lacks. AI should be viewed as a powerful tool to enhance UX design rather than a replacement for human designers—aka you should do the thinking and use AI to move pixels around on your screen.
Keen to experiment? Uizard is a cool new AI UX Design tool on the block.
AI in UX Research: A New Tool or New Partner?
AI in UX research should be viewed as a strategic partner that complements and empowers researchers. Ideal AI-powered UX research tools should offer seamless integration into existing workflows, enabling researchers to focus on high-value tasks like thinking through insights, instead of correcting transcripts. These tools should provide actionable insights, streamline data analysis, and support data-driven decision-making. They should be user-friendly, scalable, and adaptable to evolving research needs. Ultimately, AI tools should augment the skills and expertise of UX researchers, but it’s crucial to remember to check your AI-partner’s work at all times—at least today, AI isn’t infallible!
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