ux research ai

Table of Contents

Ai-driven ux research: comprehensive guide to emerging trends.

Created on Jan 30, 2024

Level up your UX process

Figma Plugins

©2024 UX Pilot AI by Adam Fard Studio

Instant insights, infinite possibilities

A guide to using AI in UX research

Last updated

22 June 2023

Reviewed by

Jean Kaluza

The rise of artificial intelligence (AI) is impacting almost every industry. Experts predict that roles and industries will change, and how we live may look very different in the future. 

It’s already integral in optimizing the workflow, minimizing time spent, and reducing the risk of manual errors.

Researchers may be able to lean into AI tools to speed up their research process, simplify methods, and boost overall accuracy. 

You should apply caution, though. Implementing AI comes with potential pitfalls that you should consider before starting. 

This guide will explain AI’s implications, benefits, and challenges to help you make the most of these emerging technologies.

• Why and how is AI being used in UX? 

AI is already impacting UX, and that’s only set to grow. 

Researchers use AI to enhance aspects of the data collection process, analysis, and the subsequent presentation of insights and findings. 

Data collection 

Collecting data from multiple sources requires significant human power to complete. Often, researchers trawl through many sources manually, such as:

Social media comments

Website analytics

Survey results

Focus group notes

Usability testing

This is a time-consuming and complex task. Performing it without errors requires a high level of expertise, but AI tools may change all that. 

These tools can collect, categorize, and organize data from multiple sources without as much need for human intervention. This boosts accuracy and speed. 

AI tools can also compile evidence in UX research . Through text-mining, sentiment analysis , heatmap tools, and more, AI can discover key data about user behavior which can feed into decision-making. 

These faster techniques boost knowledge and power across an organization in areas that may otherwise be out of reach. 

Analyzing big data 

When it comes to large data sets, manual analysis processes are outdated, sluggish, and riddled with potential errors––even if a highly-skilled practitioner performs the task. 

AI tools can analyze big data and build predictive models that humans simply can’t. 

Pairing historical data analysis with adequate training enables AI to make reliable predictions. These forecasts can be critical for planning, decision-making, and supply chain management in various industries. 

Identifying themes, patterns, and trends

AI algorithms and machine learning can spot data sets' themes, patterns, and trends through pattern recognition and anomaly detection.

This means uncovering things that humans might miss, such as: 

The precise moment customers drop off a website

Which layout option certain demographics prefer

Offering relevant, specific recommendations that convert 

AI tools can spot trends significantly faster and more accurately than humans, allowing teams to lean into accurate insights for more reliable decision-making. 

Automating UX research 

AI bots development could be significant for UX research in its entirety. AI-led user research could mean human-like bots perform UX research rather than people. 

Imagine these scenarios:

A bot runs people through usability tests

The bot asks humans questions during a virtually run focus group

A chatbot interviews customers through a live chat interface

This offers huge potential to free up time for researchers to focus their energy on creating the right questions for the bots and applying the research findings for faster action. 

Essentially, AI may make the researcher's role faster, more accurate, and more beneficial to teams overall––albeit with limitations. 

Bringing ideas to life 

Developing digital products can be expensive, time-consuming, and hit or miss. Not all ideas work for the user or succeed in the marketplace, but AI can give users a better chance to trial prototype products. 

AI tools make it faster than ever to turn a set of instructions or a basic idea into a highly-realistic image. Plus, these tools will likely improve rapidly. 

UX researchers may use AI more and more for generating new layouts, wireframing , and prototyping to get feedback faster than ever. This can save time in reworks and ensure you’re only creating valuable ideas. 

Unfortunately, this could also leave our UI-designing friends competing against AI-generated interfaces.

Presenting findings 

It’s essential to present any research findings to the broader team and key stakeholders to turn insights into action. A range of learning styles in any organization means you’ll need to present your findings in simple ways, using color, graphs, and highlights. 

AI tools can hasten and improve the presentation process by: 

Writing some of the content

Summarizing the core findings

Turning insights into easy-to-read, digestible graphs 

As efficient as this sounds, data scientists are likely to be most empowered by this while competing for roles more as the technology matures.

• What are the limitations of AI with user research?

While we can’t deny the power of AI, it’s also important for researchers and all stakeholders to recognize that AI in UX research is not a fix-all. 

There are limitations, challenges, and considerations when it comes to AI.

Let’s take the increasingly well-known chatbot, ChatGPT. The bot can produce incorrect information, harmful instructions, and biased content. Plus, people are concerned about heavy reliance on the tool.

AI tools are commonly limited in the following areas:

Context is key for accurate insights. An AI algorithm cannot understand the complete context of a situation, especially if it involves the complexity and nuance of human emotions. 

An AI tool is often not best positioned to pose relevant or suitable follow-up questions. It could also mean qualitative insights may not be as reliable as a human analysis.

Human behaviors, thinking, and emotions are not a science: They are multi-layered, changing, and challenging to understand. Human empathy is not a skill an AI tool has today. 

Yet, empathy is a critical component of research to deeply understand participants, put them at ease, and see things from their perspective. 

Flexibility

If a research session moves in an unexpected direction, a researcher can understand this move and go with it. However, an AI tool may be fixed on a certain path of questioning, making it challenging to gain new and unexpected insights. 

AI tools rely on training data to generate answers. Therefore, these tools are naturally limited in new ideas, innovation, and nuance. 

Creativity and innovation

We’ll always need human creativity and innovation regardless of how advanced AI tools become. 

While tools can perform many advanced tasks, they are no replacement for human insight, empathy, and flexibility. 

AI tools rely on training data to generate answers, meaning any creativity must be human-led.

While AI tools tend to be relatively reliable, they are not always accurate. They improve over time based on data inputs. Caution and graceful degradation should be in place in case something goes wrong. 

Researchers may need to explore how the algorithm analyzes data to fully understand and report findings.

• Benefits of AI for UX researchers 

AI is likely to significantly impact the role of UX researchers in the coming years. The researcher's role is expected to be faster, more efficient, more in-depth, and more consistent.

Faster research 

Using AI may grant researchers more time for: 

Defining the core problems

Setting the most useful goals

Asking better questions

Uncovering more beneficial insights

The most obvious benefit of leaning into AI tools is speed. Data collection , storage, and analysis are incredibly time-consuming processes. 

Shifting from manual ways of working to AI and automation lead practices will significantly accelerate the role of the researcher.

This could lead businesses to undervalue the important work of UX researchers ––something that shouldn’t be replaced by AI, given the role’s various limitations and nuances. 

Reduced costs 

Speeding up the process of UX research means fewer human hours are needed for tasks. 

This may minimize the need to outsource projects to data analytics firms, reduce resourcing allocations, and maximize time––all core cost savers for the business. 

Consistency 

Less reliance on human processes could mean that research results are more likely to be consistent across the board. AI tools may help maintain output consistency by reducing human errors and biases. Still, AI is human-trained, so biases are slipping into AI output. 

As AI is an emerging technology, UX researchers must continue managing the process and run an individual analysis as a backup in case of errors. 

To maintain a level of unprecedented consistency in research, researchers can use:

AI-powered sentiment analysis

Automated data analysis

AI-powered virtual assistants

AI algorithms

Ease of use 

AI will also impact the researcher’s workflow, bringing ease of use into the role with tools like:

Natural language processing (NLP)

Automated user testing

Predictive analytics

Automated data collection

Boosted research quantity is another positive of using AI in UX research. 

Thanks to significantly faster processes, researchers can perform more activities to discover even deeper insights about their current and potential customers. 

This may mean conducting more studies, collecting data from different methods, or analyzing more data sets. More insights mean more reliable decision-making and successful projects. 

• Tools that are already using AI for UX research

AI in UX research is rapidly evolving, and the number of tools is expected to increase significantly in the coming years. 

Many researchers already use tools to simplify their processes, boost their accuracy, and improve their research findings. 

Some existing tools include: 

.css-10ptwjf{-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;background:transparent;border:0;color:inherit;cursor:pointer;-webkit-flex-shrink:0;-ms-flex-negative:0;flex-shrink:0;-webkit-text-decoration:underline;text-decoration:underline;}.css-10ptwjf:disabled{opacity:0.6;pointer-events:none;} Uizard

Using the power of AI, Uizard helps UX teams mock up designs in minutes.

Mocking up products is simpler than ever before with features like:

Turning screenshots into editable designs

Scanning sketches to automatically generate designs, prototyping , and wireframing

Uizard helps research teams gain faster insights from users to ensure the products produced are fit for purpose and delight the end customer. 

Recruiting participants for UX research can be one of the most challenging aspects of the process. UserZoom helps researchers do just that. 

An AI-powered recruiting engine for participants automates the process, helping teams source research participants and customers from across the globe. 

Synthetic Users

Limited testing time, small budgets for testing, and participant recruitment challenges can all be highly restrictive for UX teams.

Synthetic Users is still in beta and has yet to release. Through the power of AI, it promises the chance to test products with AI participants. 

This will help UX researchers discover insights, identify pitfalls, and optimize products without a big budget, timeline, or real participant group. 

Amped Research  

Many researchers require a research assistant to take notes, search through data, and analyze findings. Amped Research is essentially an AI-powered research assistant.

Currently waitlist only, OpenAI GPT-3 powers Amped, and it can generate insights and summaries. It also sends automatic updates to stakeholders and assists with presenting findings for reduced paperwork and faster action. 

Dovetail leverages the power of AI to move teams from insights to actions in record time. The new workflow will hasten manual tasks and offer tools to analyze data even quicker. As part of this, AI will reduce bias and errors for more reliable insights. 

For example, you’ll soon be able to summarize lengthy conversations into core bullet points. AI can also automate draft insights, discover related trends, and increase accuracy for classifications in the future. 

Rather than replacing how customers work, Dovetail uses AI to support teams in their projects. 

• Cautions and AI best practices in UX research

Due to the limitations of AI in UX research, researchers can’t expect an AI tool to fulfill all tasks. Overreliance on AI tools can be problematic. 

If researchers feed an AI tool incorrect or biased information or don’t train it sufficiently, the output will be unreliable. This could lead to negative consequences. 

AI tools are still developing, so we shouldn’t see them as solutions for all tasks or pillars of accuracy. Human discernment is still critical as this technology progresses. 

Using AI tools? Remember: 

They’re not always accurate

They improve over time and may be more reliable in the future

They do not necessarily replace human inputs

They aren’t a replacement for human empathy, nuance, or creativity 

• How will AI impact the future of UX design and research?

In the coming years, AI is likely to significantly impact all industries. We expect big changes in UX design and research. 

AI in UX research will likely: 

Increase personalization for customers

Boost data-led decision-making

Rapidly speed up the design process

Improve research reliability and insights

In the day-to-day, AI may reduce menial tasks for researchers, granting them more time to create questions, set appropriate goals, and produce improved results. 

Holistically, AI could boost UX research for better, more usable products, ultimately helping teams create more satisfying products for users.

Will AI replace UX researchers?

While AI will significantly affect the UX research process, it's unlikely to replace UX researchers. Rather, we expect it to automate certain aspects of the role and speed up processes. 

It’s essential that organizations still value the role of UX researchers. The results of UX-performed research depend heavily on empathy and the right questions written by humans. 

Humans can do things that AI can’t, like:

Design research studies

Provide nuance and context

Interview participants with empathy and understanding

Consider ethical factors

Generate creative ideas and solve problems

How long does the UX design process take with AI?

While AI can speed up UX design processes, how long it takes depends on many factors:

The requested task

The information you give to the AI tool

How advanced and relevant the AI system is

The specific project requirements

The more complex the project, the longer it will take. 

How to use AI in the UX writing process? 

To improve efficiency in UX writing, you can use AI tools to: 

Generate content

Improve the speed of content writing

Make language suggestions

Provide a consistent tone of voice

Consider accessibility and inclusivity

Optimize content for SEO.

However, AI writing bots have limitations. They may provide incorrect, biased, or inconsistent content. That’s why it’s important to check any AI-generated content for accuracy.

Should you be using a customer insights hub?

Do you want to discover previous user research faster?

Do you share your user research findings with others?

Do you analyze user research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 January 2023

Last updated: 3 July 2023

Last updated: 20 March 2024

Last updated: 24 June 2023

Last updated: 29 May 2023

Last updated: 6 March 2024

Last updated: 22 August 2024

Last updated: 26 February 2024

Last updated: 30 April 2024

Last updated: 12 April 2024

Last updated: 26 March 2024

Last updated: 13 May 2024

Last updated: 28 June 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next, log in or sign up.

Get started for free

• Artificial Intelligence
• Product Management
• UX Research

Best AI Research Tools: Insights & Recommendations

In today's AI-driven world, the excitement about artificial intelligence is widespread, with numerous tools available to shape our lives and the world. But with so many options flooding the market, it's easy to feel overwhelmed.  Our blog post guides you through the maze of AI tools. We'll uncover the hurdles of current AI-powered research tools and spotlight the most promising ones to keep an eye on. 

Overall, our expectation of AI is clear: to tackle our work’s tedious and monotonous aspects. Let’s imagine, for example, that AI would relieve us of the tiring task of transcribing hours of interview recordings or that it would sift through massive data sets and generate insights and visualizations within seconds. The dream scenario: AI frees us from repetitive tasks and allows us to focus on what’s really important – innovation and creativity.

But the question arises: How useful are the currently available tools, and what challenges do they generate? We delved into this topic at the studio to understand the current state clearly.

How we approached AI research tools

Our mission was to investigate the reliability of current design and research tools thoroughly.

We formulated a dedicated team consisting of three researchers and three designers. While some team members immersed themselves in articles and courses, others extensively tested AI research and design tools within the given timeframe. We filtered through numerous tools to identify the most promising ones . Then, we rigorously tested AI tools for UX research to evaluate their suitability for future integration and understand their limitations.

Read on to get a sneak peek at our research team’s conclusions.

Please note that we only focus on AI (assisted) research tools in the following section. There are many types of AI tools, they know different things, and they are trained differently. This blog post is not about AI tools in general but specifically about UX research tools.

5 points to keep in mind when working with AI research tools

While AI tools provide various functions, it’s crucial to acknowledge their constraints. Although some speculate they will eventually replace human work, mirroring human cognition, our experience shows that this isn’t happening yet. 😉 

To leverage the potential of AI tools in the research process, there are some key points to keep in mind when using such tools. 

1.Double-check the output of AI tools

Based on our experience, we strongly advise double-checking the output of AI tools for several reasons: AI’s lack of contextual awareness, its potential for varying weight assignments to information, its reliance primarily on textual data, and its tendency to provide overly general responses.

• AI is not aware of context AI may struggle to identify the information that truly matters because it can’t grasp the broader context of the project. The tools we tried out did not enable us to clarify the objectives and research goals, which sometimes resulted in irrelevant outputs. Example: We uploaded a user interview transcript into a tool that creates summaries and analyses, turning qualitative data into insights. At one point, the participant went a bit off-topic, that was not strongly connected to the main research objective but was still interesting. Since the tool was not aware of the research objective, it highlighted this part as one of the most important insights. The tool let us edit the output easily, but this situation highlighted the need to carefully review automatically generated insights.
• AI might analyze the information differently Attention mechanisms allow AI models to weigh between information, typically by assigning greater importance to more frequently mentioned elements.Example: The AI tool tested generated the transcript and extracted the most important statements and findings based on the recording of a usability test. The tool identified the search function as the biggest issue. The plain text (transcript) did indeed suggest this pattern since the word “search” and how it didn’t work as expected was mentioned several times. However, as a researcher observing the whole session, I could easily see that the participant had far more difficulties uploading a document. Although it was mentioned only once or twice, its severity compared to the search was clear, which the AI tool couldn’t necessarily assess.
• Typically relies on textual data AI research tools rely on textual information and thus struggle to capture the full context of user behavior. They can miss subtle nuances or specific user contexts that human researchers can intuitively understand. Their limitation mainly lies in their inability to effectively combine and process non-textual information (e.g., tone of voice, time on task).Example: children usually agree with almost everything an adult says in a test session. They also tend to say positive things about the product and features. But their faces are like mirrors, revealing everything. An AI system processing the video transcript of a usability test with children concluded that the product is “likable, easy to use, and appealing.” But while watching the session, the children’s faces and non-verbal cues made it clear to us that there were struggles.
• AI tends to provide general answers AI tools provide generalized answers because they are trained on large data sets to capture different contexts. While this enables them to offer generalized answers efficiently, they may lack the detailed understanding (nuanced specificity) that human experience and contextual knowledge can offer.Example: Creating personas using AI would make researchers’ lives much easier. The idea of generating content and connected visuals sounds amazing and time-saving . However, our experience with AI-based tools revealed a common drawback: the generated content often lacked specificity. For instance, when creating a B2C persona for engagement ring buyers, the AI output was generally correct but couldn’t provide nuanced insights. It overlooked the sentimental value of the process (e.g., the fears that the partner won’t like the ring or will say ‘no’). While the tool allowed manual editing, refining the persona took almost as much time as creating it without AI.

✨💡 Tip: AI research tools offer a strong foundation but often rely on single input sources, usually text. Remember that qualitative data analysis is complex, requiring a holistic view, including implicit meanings and nonverbal cues. So use AI smart. Check the generated output, and add your own point of view to it. 

2. Count with the limited creativity

AI tools are good at processing information within the parameters of their training datasets, efficiently analyzing patterns. However, their strength lies in complementing human creativity rather than replacing it, as they may not generate truly innovative or out-of-the-box ideas.

Example: On a project, we needed some out-of-the-box ideas on how to proceed with research to show its value to our client, who believed they already understood their target group and market completely. We turned to the internet for ideas about how to approach the situation – read blog posts and articles and also tried out AI tools. However, both Google and AI provided similar approaches, which, while not bad, lacked the unconventional approach we really needed.   In the end, it was the collaborative brainstorming sessions with my colleagues that provided the innovative solutions we needed. AI may offer valuable input, but it’s our team’s creativity and diverse perspectives that truly shine in problem-solving.

✨💡Tip: if you need a creative idea or innovation, an output generated by an AI tool can be a good starting point, but never be satisfied with it! Keep thinking about the output you received and discuss it with your colleagues!

3. Be aware of AI hallucinations

AI hallucination occurs when artificial intelligence produces inaccurate or nonsensical outputs. This often results from biases in the training data or the AI model’s contextual comprehension limitations.

Example: I asked a question from a popular AI tool and got an answer that was a bit strange at first glance, so I asked it to give me sources for the provided information. I started to check the references, but 2 out of three did not exist. 

✨💡Tip: critically evaluate the output and consider the context in which it was generated. Also, try to verify the outcome with other sources or references . 

4. Make informed choices when it comes to AI research tools

While we know that marketing texts can often be misleading, this seems to be especially true around AI currently. 

The word “AI” attracts attention and makes people believe that something that is AI should be better than something without AI. Consequently, based on our experience, many tools on the market emphasize their AI features. But, once you try them, you realize that they provide the same as before the AI-hype; they just added the word AI somewhere on their platform. 

✨💡Tips: Numerous companies use the word ‘AI’ to boost user numbers without adding actual value. Before trying a new AI feature or tool, check its reviews, research the company, and look for information on the AI mechanisms used and how they are integrated. 

5. Consider and treat AI as a junior research assistant  

Although artificial intelligence is very different from human intelligence , and they do not have a human-like nature (yet), for example, they lack emotions , abstract thinking , and creativity . In an important aspect, they are very similar to humans: They are not infallible, they can make mistakes! 

They have a lot of knowledge but less experience in applying it to new situations. Just like a junior assistant who is very talented and hardworking but hasn’t yet had the opportunity to put together the small pieces she has learned so far.

Like the assistant requires time to accumulate experience, AI also requires time to improve.

Until it happens, we can use their vast knowledge effectively, but we must be actively involved in the process and carefully examine what they do.

Which AI tools do we recommend you try out? 

Despite all the limitations, there are tools based on our experience that can effectively help research processes. 

As mentioned before, use every AI tool as an assistant who does its best, but without sufficient experience, his performance is limited. Still, they can save a lot of time and give you opinions, overviews, summaries, and ideas to work on further. But a lways remember to double-check the output they generate.

Here’s a short list of tools we recommend you try!

Papertalk   – for discovery and desk research

• What can it do for you? – Summarising papers and documents and extracting their key points. – Generating actionable insights. – Organizing the papers and allowing you to find what you need easily.
• What to pay attention to? – The chatbot feature seems nice, but it answers in a very generic way that is not that useful in many cases. – Sometimes, it gives a very short summary, and unfortunately, it is not editable. 

Personadeck for persona creation

• What can it do for you? – Creating personas with AI. – It puts the persona in a simple but well-structured template that can be edited and fine-tuned easily. – They promise that B2B personas are coming soon. 
• What to pay attention to? – The output is a bit too generic in itself, for sure, it needs some fine-tuning. – It has some usability issues, e.g., creating multiple personas or modifying the prompt is not as easy as it could be.

Fillout for quantitative research

• What can it do for you? – Creating a form/survey based on the topic you provide. It can be auto-generated or created based on templates. – Checking the result with a data analytics dashboard. – It has integrations for various tools.
• What to pay attention to? – Having suggestions would have been a nice extra with which AI could help more to have the opportunity to select from different options. 

Notably for qualitative research

• What can it do for you? – Analyzing and summarising research materials (transcriptions, documents). – It also lets you choose a template to put this content in a format of your choice. – Creating transcripts for uploaded video recordings.
• What to pay attention to? – AI-generated insights will appear on a Miro board with sticky notes, but the sticky notes are not organized. You have to edit it manually to get a presentable output from it.

Kraftful for qualitative and quantitative research

• What can it do for you? – Transforming qualitative data into insights. – Additionally, it lets you browse through and ask questions about the insights in the AI-powered search bar. – Build surveys (auto-generate them or let you use templates).
• What to pay attention to? – Visual parts were missing from the product (e.g., the possibility of creating a journey, a flowchart, or data visualization) that could make the result more presentable. – Chat functionality was a nice-to-have, but it was limited and gave repetitive answers. 

+1 ChatGpt or Copilot as a source of ideas and inspiration

• While ChatGpt or Copilot are not dedicated UX research tools, both can be useful to get a general overview of a topic. Even if they do not provide you with unique answers, they usually give a comprehensive output from which you can select what you think is beneficial and useful. They can also serve as effective starting points for further brainstorming.

What about the future?

Although AI technology is advancing, it has yet to reach the level of human cognition and understanding. We do not know how long it will take for it to overcome this challenge, BUT 

We believe that collaboration between humans and AI will be the driving force behind successful research. For this collaboration (between AI and human researchers) to really lead to the best results in the future, we need to constantly observe and monitor the evolution and capabilities of AI research tools.

By using them, it will become clear what kind of tool we actually need and where we can harness the power of AI in the research process. Therefore, it’s also in our interest to explore these tools. Without researchers, AI research tools will never be able to meet our needs.

After taking our first steps with AI research tools, more AI experiments will follow.

What has been your journey with AI research tools?

Want to learn more?

If you want to read more about AI and UX design, UX research , and our experiences, make sure to check out our articles and related case studies .

Do you need help with designing AI interfaces? Book a consultation with us. We will walk you through our design processes and suggest the next steps!  

Let's talk

• Book a Demo

Building AI Features: A 7-Step Guide for UX Researchers

How do you do UX research for building an AI-powered product/feature? Read this detailed guide for expert tips, advice from the Looppanel team, and a handy template to work it all out.

Heard about the AI bot that decided to be a stand-up comedian? 

It kept telling jokes...but they weren’t funny.

I guess some things are better left to humans after all.

Generative AI tools like Chat-GPT and DALL-E have caused a lot of excitement around these parts. For product designers and researchers, it’s incredible stuff to behold. Poems, essays, videos of cats cuddling in front of the Eiffel tower— you name it, AI can generate it. 

But amidst all this hype and awe, there's an important question we need to ask—are we getting a little ahead of ourselves by dreaming up all the possibilities, without doing due diligence first?

AI may be incredibly powerful, but it's not a magic solution that can solve every problem right out of the box. It’s really useful when applied right, but potentially chaotic or even dangerous if used recklessly without regard for consequences.

This is why user experience (UX) research has never been more vital for the industry.

As companies race to integrate generative AI into their products and services, UX research ensures that we're introducing these powerful capabilities thoughtfully and responsibly into user experiences. Randomly injecting AI without context is a surefire way to build solutions that underwhelm at best, and infuriate users at worst.

When done right, AI-powered tooling can feel like magic. But building it needs proper groundwork, with meticulous research and design.

We have experience with building something like that. 

Have you checked out our AI-powered research analysis & repository tool Looppanel yet? It even auto-tags your notes! Although in the age of GenAI, the magic is how it auto-tags your notes, and where it brings the researchers into the mix.

Consider this article your go-to guide on running UX research for building AI-powered features and products. You can find a detailed template to help you through every step of the process below, along with insights on:

• Identifying the real user problems and inefficiencies to solve
• Understanding the current capabilities and limitations of AI
• Determining which tasks should stay human-centric vs. automated
• Building user trust and mental models around AI capabilities  
• Evaluating and iterating on AI outputs through usability testing
• Practical tips like prompt engineering and considering technical constraints

UX Research for building AI-powered software

Before diving headfirst into building an AI-powered feature or product, take a step back and do some critical groundwork. 

Randomly adding an AI-powered feature is a surefire way to waste resources and create underwhelming experiences. 

Instead, start with a deep understanding of the user's needs, wants, and pain points. 

Step 1: Identify the user

Research’s role: Identify the types of users in your category and who you want to go after. Make sure you’re aligned with your team.

Even with the most exciting technology involved, the first step of UX research remains the same.

Think about the user. 

This may sound obvious, but it may be the most crucial step in your work. Very different people doing the same job have very different expectations. This has always been true of software—you have Figma, and then you have Canva. Both are design softwares, but built very, very differently.

Understanding your core user persona becomes even more critical in an AI-powered workflow, where you’re constantly deciding what the AI should do, and what counts as good output.

Do NOT skip this step.  

Step 2: Identify the user’s problems

Research’s role: Do generative research and dive deep into knowledge about the user base, their workflow, and pain points. Make sure your team is aligned on these.

Once you know who you’re focused on, it’s time for strategic research to truly understand them. Jared Spool has a lot of notes on this, read them here and here .

Start with closely observing your users in their natural environments and workflows. Look for the parts where they seem to be spending an inordinate amount of time, or getting frustrated. 

Maybe they've explicitly complained about a certain task being a huge pain point. Perhaps you've noticed that they dedicate 80% of their time to one part of the process.

Such strategic thinking also gives your product a massive competitive advantage. The advent of AI tooling has made it much easier to code, develop apps and build websites from scratch in a couple of hours. Take any use case and do a simple Google search— you’ll find at least 20 tools jostling for attention and promising the same outcomes. In such a landscape, the only way to stand out is attacking the RIGHT problem the user cares about—even if they don't know it yet. 

So, how does one find the right problem?

Look for signs that users are willing to dedicate a large amount of time, money, or effort to a task. The amount of time or money they’re currently spending, is a clue to you about the value of that task to the customer.

For example, we spoke to many, many researchers about their research workflows. We asked them—what takes you the most amount of time? We noticed what users were actually doing on the product—where were the inefficiencies?

Time and time again, it became clear that even with our AI-generated notes to speed up analysis, researchers were spending hours, if not days tagging their data. They would often struggle with creating a taxonomy, and if multiple people were tagging together consistency became a challenge.

Clearly this work was important—researchers were willing to spend significant time manually tagging 100s of notes—but it was also repetitive and painful. 💡Painpoint identified .

Step 3: Understand AI’s capabilities as a tool

Research’s role: Align the team on AI’s capabilities—what it can and cannot do well.

Once you’ve pinpointed a genuine user problem or inefficiency, the next step is to assess what's actually possible with AI given its current state and capabilities.

Keep in mind—the AI landscape is constantly evolving, so the answer to this question may change in two months. While early AI releases primarily operated on text-based data, recent  iterations can now process multi-modal inputs like images, videos, and even audio recordings. 

That being said, there are still some general rules of thumb for what AI can and can’t do:

AI excels at:

• Processing and summarizing large volumes of data, especially text data
• Generating standard content types that are commonplace like emails templates or code snippets
• Repetitive tasks that require limited context

The key is to i dentify where AI is obviously better , and focus instead of work which it cannot do , such as:

• Understanding nuance—picking up the human elements of an interaction
• Creativity and originality that lets you write a hilarious joke, or a beautiful poem
• Building empathy and human connection that enables your research participants to open up about their lives during an interview
• Strategic, high-stakes decision-making where complex reasoning is required
• Work that requires having contextual information about your stakeholders, company priorities, and which experiments you already tried last year—information the system simply doesn’t have

Identify users’ pain points that lie in the first bucket (what AI can do), and let them keep doing the work that lies in the second (what AI can’t do).

For example: AI can be useful in helping people find the answer to a question on your support page. But it’s not so handy at calming down an irate customer who’s been waiting on a support call for 35 minutes. At least so far.

Step 4: Defining the Solution

Research’s role: Figure out where your specific user persona needs efficiency versus control in their workflow.

There are many ways to make a video.

If you’re a film-maker in Hollywood, you probably want Adobe Premiere Pro. These are multi-million dollar films—you need control over every frame, every screen.

If you’re an Instagram health influencer making sponsored ads, you just need something that allows you to change the lighting and filters to look just right , without having to understand the details of exposure and light theory.

If you’re me and you don’t know the first thing about creating videos, you’ll google “Video AI”, pick the first tool you see and expect AI to do most of the work.

AI is actually useful for all 3 of these users and use cases—but in fundamentally different ways.

A filmmaker in Hollywood may use AI to create the first draft of captions, or color-correct screens. They want control over which takes to keep and which angles work best. Or they might need it for highly complex, and specific use-cases like de-aging Harrison Ford.

An Instagram influencer may use it to create a first draft reel that they tweak for lighting. They want control over how they / the food / the clothes / the suspicious-looking weight-loss shake they’re selling looks. Weight-loss shake is looking an icky color of green? That needs an edit.

Me? I don’t know the first thing about lighting, transitions, or timelines. I want AI to do almost everything, with a veto power to edit / remove something I hate.

Your job as a researcher is to figure out what your user’s priorities are. Where do they need control, and where do they need efficiency?

There will always be aspects of a workflow or creative process where human expertise, judgment, and real-world context are invaluable—- and frankly, irreplaceable by AI. What do those look like for your users?

Do users need transparency or a black box?

Researchers role: Identify how much oversight and control your user persona needs for their use case.

Here's a great question to start with: How much transparency and user control should your AI tool provide?

On one end of the spectrum, you have tools that offer some degree of transparency, where users can see and influence every step of the AI's process. This model involves using AI as an assistant and force-multiplier, handling tedious computational tasks with speed and precision, while the human provides contextual expertise, strategic thinking, and oversight. The key is that the user always feels in control and understands what the AI is doing.

For complex, high-stakes decisions like—"Which user problem should we focus on?", "Which takes should we keep in this $100 million movie?", or "What's the right positioning strategy for our business", you want AI tooling that gives up some amount of control in the workflow. Where the AI contributes and where the human takes over depends on what parts of the process are sacred to the user persona.

If your user persona prioritizes simplicity or speed over control, you may be able to take a black box approach.

Take me for example—I truly don’t know what camera angle to pick, so it’s easier and faster for the AI to take control away from me. I don’t care for the output to be 100% perfect, so I can afford to roll with its take. For me + video editing, a black box approach is 🤌. 

Integration is key

Researchers role: Identify the tools and workflow of the user before and after they use your AI features. Whether you’re a filmmaker or an influencer, you probably want your video to go somewhere once it’s ready.

This is where understanding user workflows is crucial. Assuming the creator isn’t generating content just for the sake of it, they need to pull it out into some other tool or share it with an audience.

Think of an AI writing assistant for example. It’s introduced with the goal of enhancing your creativity and output quality. But if the AI assistant exists as a separate, disconnected tool from your preferred writing application like Google Docs or MS Word, you'd constantly have to context-switch, likely breaking your creative flow.

As a researcher it’s your job to identify the user workflow before and after your solution and make sure your product works seamlessly within it.

Step 5: Prompting!

Now comes the actual step where you figure out the AI prompt(s) that solve your user’s use case.

Here are a couple of guidelines for this step.

1. Have the target user create the prompt.

Yes, you read that right.

Prompting is actually no longer highly technical—anyone can do it. In fact, it’s better if technical folks don’t do it.

The challenging thing about prompting is knowing what “good” output looks like. AI can create so many types of videos—when is it good enough?

As discussed, the answer to this question depends on your persona. What may look like a really cool video to you and me, may be thrown out at a glance by a professional videographer.

This means that having the actual user in the driver’s seat, experimenting with prompts and deciding when they feel good enough, and when they feel magical , is really really helpful.

It allows you to try out, reject and iterate on prompts much faster than a traditional build, test, release workflow.

If you don’t have access to your user persona, get someone who really deeply understands them to do this step (but this is not ideal).

2. Keep technical constraints in mind.

Your user wants the moon. Great, you know what the solution is.

But your tech team can only release half a moon today and the other half in two weeks. That’s okay—it’s just important for you to know as you’re testing prompts.

What are the limitations we’re dealing with? What kind of data do we get? What kind of data do we need to output? Where can we introduce human interaction?

Understanding your limitations upfront will help your team stay aligned and prevent those pesky we-actually-cant-build-this conversations after you’ve done all the work.

Speaking of human interaction…

3. Where’s the human in the loop ?

(pun intended)

The cool thing about AI is that you can totally customize how you work with it.

You could hand it a transcript and say, “tell me the insights” (not recommended), or you could hand the same transcript one paragraph at a time and ask it to summarize just that.

The way you design the workflow again depends on the level of control your user persona needs to get an output they consider magical. 

The same thing applies to context—when do you ask for it, and how much do you ask for? 

You could generate a video with a one-sentence prompt, or you could ask your user to input 10 highly specific data points to get a more tailored output (Who is the audience? What style do you want? Is this for YouTube or Instagram?).

This is where knowing your user persona becomes crucial. That will help you decide where and when the human should be in the loop.

4. Keep track of prompts you test

Version history is critical. You’ll be testing many, many, many, many iterations—knowing which worked, which didn’t, and why is crucial to saving time and not losing track of that prompt that FINALLY worked!

Here’s a template to get you started .

Step 6: Testing, testing, 1 2 3

With some promising AI-powered concepts in hand, the next stage user testing and validation.

Heard of the Wizard of Oz? Not the old movie with Judy Garland, but a prototyping technique of the same name.

With the Wizard of Oz technique, the core idea is to create an experience where the AI's role is simulated by a human "wizard" behind the scenes. Basically you have a person do the work you’ll eventually automate to see how users react.

You can use to varying levels:

• Have a person do the entire workflow instead of AI
• Have AI do the prompt work, but let people manually feed the prompt in, and copy the output out

This allows you to thoroughly test the overall experience, user flows, and uncover potential points of confusion or distrust while minimizing the amount of code written.

If it’s hard to test these workflows with real data at the prototyping stage, you can also release features in beta and work closely with users to see how they work with your AI output.

It’s crucial to let users try the AI features in real-world settings with real use cases, rather than as a cool toy to play around with—the feedback and usage you’ll get will be wildly different.

You treat a blog post auto-generated by AI very differently when it’s for fun (“So cool!”) versus when it needs to be published on your website (“This sounds like a robot”).

During your testing phase, in addition to evaluating usability and flow of your product (where applicable), make sure to probe users on:

• Discoverability of AI features (we got burnt by this a couple of times!)
• Their level of trust in the AI's outputs or recommendations (and how to build it)
• Points where they wanted more transparency into the AI's reasoning
• Where human input would be most useful
• What they do after they’ve got the AI output

Iterate and refine the prototypes based on this feedback until you arrive at an experience that works for your audience.

Then pick the next problem, and start again!

Step 7: Enable the feedback loop  

As you move closer to launching your AI-powered feature(s), build in mechanisms for continuously capturing user feedback. This feedback loop will be critical for promptly identifying issues, managing user trust, and ensuring your AI system keeps learning and improving over time.

You could start with a simple upvote / downvote system and a simple dialog box to collect mass feedback.

But frankly, our favorite way of collecting feedback: talk to your customers. Especially in an all-new-world, you want to deeply understand what your users are doing in your system, outside the system, and why.

On building trust through transparency

When designing an AI-powered product or feature, one of the biggest challenges is getting users to actually trust the AI enough to use and adopt it.

Think about it—you're essentially asking people to hand over tasks and decisions to a machine intelligence that may or may not get things right all the time. That's a big leap of faith to make, even for the most tech-savvy users.

When it comes to building trust—be it within personal relationships or business teams, there are four pillars. The brilliant designers at Ericsson created their own version of this, within the context of human-AI relationships.

1. Competence

First and foremost, your AI tool needs to demonstrate clear competence in its core capabilities. Users need to see proof that this thing can actually deliver on its promised value.

You can prove your system’s competence to the user by using these techniques:

• Explaining why the system generated a specific output and how confident it is in its quality
• Letting the users test the AI system in a quick and safe way.

2. Benevolence

In this scenario, it means openness and transparency. Once competence is established, reinforce that your AI means no harm and has no hidden malicious intent. It's simply a tool created to make the user's life easier and more productive.

You don’t hire the intern who ignores your feedback. Users won’t trust an AI system that ignores their feedback.

You can show that your system is open to change by:

• Giving users easy ways to edit or undo an action taken by AI
• Enabling the system to take feedback from users and change its output accordingly

Don’t forget to build in human checkpoints or approval gates for especially critical tasks or decisions , where the AI's output must be manually reviewed and validated before being acted upon. ‍

3. Integrity

Similarly, integrity is about ensuring your AI experience operates within clear ethical guardrails that align with your user's values and principles.

For example, if you're building an AI that interacts with private user data, it's crucial to be upfront about how that data is handled, used, and protected. Break that trust through unethical behavior, and users will abandon your product instantly. 

It's also crucial to be upfront about what AI can and cannot do from the very start . Don't try to present it as an infallible, all-knowing system. Instead, acknowledge that AI may occasionally make errors.

If your AI feature is built only for certain use cases, that’s okay—just tell the users upfront.

4. Charisma

Now, this doesn’t mean that you steal Scarlet Johansson’s voice to bring Hollywood star charisma to your bot. That’s not the lesson here. 

Weird name aside, this pillar is actually not that different from what we do with product design today. Basically, we want UIs to be visually appealing and easy to use. We want content on your UI to be in the tone of voice your customer expects.

The key is making your AI tool feel like a welcome addition to the user's world, not an impersonal set of utilities to begrudgingly put up with.

Template : 7 Steps to Run Research for AI Features

We’ve created a detailed template you can use easily duplicate and use for your own research on AI features.

It takes you through all the steps detailed above, and includes brainstorming spaces, checklists and all the essential notes you need on building AI tooling. You can thank us later.

Get Looppanel's FigJam Template for '7 Steps to Run Research for AI Features' here.

Get the Miro Template version here.

To sum it all up

Alright, let's wrap this up with a bow, shall we?

The key takeaway? AI might be the shiny new toy in the tech playground, but it's not a magic wand. Like any tool, its value lies in how well we wield it to solve real user problems. And that's where UX research comes in. A few points to remember.

• It's not about cramming AI into every nook and cranny of your product. It's about finding those sweet spots where AI can genuinely enhance the user experience. Maybe it's automating tedious tasks, providing intelligent suggestions, or processing data at superhuman speeds. Whatever it is, make sure it aligns with your users' needs and workflows.
• Get your target users involved in crafting prompts. They know best what "good" looks like in their world.
• Trust and transparency is key. Be upfront about what your AI can and can't do. Show users how it works, let them peek behind the curtain, and give them an escape hatch if things go sideways.
• Testing is crucial. Whether you're going full Wizard of Oz or releasing beta features, make sure you're getting real-world feedback. And once you launch? Keep that feedback loop flowing. Your AI system should be learning and improving.

In the end, successful AI integration is about finding that perfect balance - between human and machine, between efficiency and control, between innovation and familiarity. It's a tightrope walk, but with solid UX research as your safety net, you'll be wowing users in no time.

If you’re interested in learning more about designing with AI, read Google’s People + AI Research (PAIR) guidebook . It's chock-full of practical guidance for designing human-centered AI products, based on data and insights from over a hundred Googlers, industry experts, and academic research. Happy researching!

Table of Contents

Get the best resources for UX Research, in your inbox

More from looppanel, how to use ai for qualitative data analysis.

How to use AI in UX Design & Research

Is Your Hair on Fire? A Guide to Discovering User Problems

How to Do UX Journey Mapping: A Detailed Guide

The Best AI Tools for UX Research & Design

The team at Marvin scoured the web to find the best AI tools for you to integrate into your UX research and design workflows.

“AI is a brilliant tool for people to be more productive.”

Don’t take it from us. That’s Bill Gates speaking at Davos 2024. 

As AI pervades industries across the globe, it’s already making headway in UX. Several new  tools are popping up out of nowhere. Existing ones are adding AI capabilities to their product offerings. Recently, we examined the transformative impact of AI in user research .

However, some researchers and designers are still skeptical of AI use.  

We understand their reservations. We’re here to convert the non-believers. AI will never replace human researchers and designers. Of that we’re convinced. It is also capable of augmenting our work so we can focus on deeper, more meaningful analysis.

We’ve scoured the web for the best UX AI tools for you to integrate into your workflow. Here’s what we’ll cover:

• Benefits & Limitations of AI in UX
• Where to Use AI in the UX Design Process
• Choosing the Right UX Tool with AI
• Top AI Tools to Augment UX Workflows
• Best Practices for Integrating AI into UX

Get ready for helpful tips, tricks and tools to elevate your research and design.

TL;DR – List of Top 8 UX AI Tools to Consider

AI for UX design promises to revolutionize the way that designers and researchers work. It generates greater efficiency and frees up a UX professional’s time for deeper analysis. In this article, we present eight of the best UX AI tools for different stages of the design process. We’ll dive into how each of the following tools leverage AI to improve the UX workflow from beginning to end: 

• Adobe Sensei
• Attention Insight

ATTENTION: All designers and researchers. Use these AI tools to supercharge your toolkits.

What Are UX AI Tools?

UX AI Tools are digital platforms that use artificial intelligence to aid in product development. 

Product designers can implement these applications throughout the research and design process.

Use UX AI tools to help manage all design activities. Generate new content and ideas when beginning a project. Designers use AI-powered UX software to create wireframes or prototypes for user testing. AI can collect, process and analyze large swathes of research data much faster than a human. After testing prototypes with users, AI UX Tools provide suggestions to improve the product experience and flow.

Implementing AI UX Tools increases the productivity and scale of design. Analyzing more data from a wider range of stakeholders allows researchers to conduct deeper analysis. Leveraging AI can greatly enhance the creativity of a company’s design practice. They facilitate collaboration across an organization, creating a user-centric culture.

AI UX Tools help designers create more intuitive and user-friendly designs.

The Pros and Cons of AI in UX

AI promises to have a big impact on a UX teams:

• Productivity & Efficiency – Designers and researchers can automate mundane tasks to improve productivity. Automation saves time and human effort. This accelerates a product’s development time.
• Financial Standing – Outsourcing certain tasks to AI reduces designer and developer costs. This impacts overall profitability.
• Creativity & Agility – AI’s ability to endlessly churn output allows designers to generate several new design concepts. Overcome design bottlenecks with AI. 

Let’s further examine the benefits and limitations of implementing AI in the UX process.

Benefits of Using AI in the UX Process

Traditional research methodology involves manual and tedious work. It’s slow — taking up a huge chunk of a researcher’s time. Not anymore, thanks to AI.

The benefits of using AI in the UX process are aplenty:

• Workflow Automation . AI automates repetitive and mundane tasks such as scheduling interviews and sending notifications. It can also conduct more advanced tasks such as user testing and data analysis, allowing researchers and designers to focus on more strategic work.
• Automated Data Collection . Collate data from multiple sources such as social media websites, website analytics, surveys, focus groups and usability testing. Tools can categorize and organize data without the need for human intervention.
• Real-time Data Analysis & Insights. Analyze large datasets faster with pattern recognition and anomaly detection. AI uncovers trends, patterns and insights missed by humans. It also analyzes data faster and more accurately than humans. This allows for a deeper understanding of user habits, preferences and needs. 
• Reduce Human Bias & Error . Manual transcription and traditional analysis gives rise to human error. AI mitigates this by removing human involvement until it’s necessary.
• Personalization. Companies continuously observe and record user behavior. AI algorithms can handle large volumes of this user data. AI provides customized recommendations tailored to various users After analyzing people’s preferences. With AI, interfaces feel tailor made for each individual.
• Predictive Capabilities. AI can build predictive models that humans simply can’t.  Algorithms anticipate user behavior and preferences with high accuracy. This opens the door for more intuitive interfaces and seamless experiences.
• Content Generation. Generative AI tools can help designers quickly create prototypes and wireframes. Options can be customized to different user groups. AI tools also offer writing assistance in creating audience-specific copy for product content. 

Limitations of AI Design & Research Tools

Leveraging the full potential of AI requires a deep understanding of its inner workings. How does it utilize data and impact users’ lives? As with any technology, it’s important to understand AI’s constraints: 

• Context – AI can’t understand how output feeds into study goals or research questions. AI has no background information about the product or users, and it doesn’t lean on insights from previous research. Systems don’t know what factors are most important to the researcher. They can’t ask pointed questions and alter the course of interviews. 
• Loss of Human Touch – AI lacks basic awareness of human psychology. It doesn’t have human empathy, creativity and emotion. For instance, in logo design: AI may be able to suggest color palettes. What it CANNOT do is capture the subtleties of human emotion – the type of reaction it evokes in users. 
• Ethical Considerations – All AI tools are trained on biased datasets (systematic bias). Any tools that claim otherwise are just plain wrong. Using a non-representative dataset introduces statistical biases. AI doesn’t discriminate and uses biased input if provided with it. It falls on the researcher to take this into consideration and produce bias-free insights. 
• Noisy data – AI algorithms are constantly referred to as a ‘black box’ – we might never know their inner workings. Some tools generate content without attributing the source of the information. A lack of citation means that AI output can’t be validated. Furthermore, it can be influenced by questionable sources (plenty of those out there!) and throw out inaccurate output. It also can give you two different answers for the same question –  there’s no consistency . A lack of transparency around the process introduces the element of doubt in AI output. Validate AI powered output before using it in analysis. 

AI will always need humans to corroborate results. Researchers and designers must ensure that AI has carried out its task correctly and fairly. Humans will always be responsible for final decision-making.

For a deeper dive, here’s more on the merits and demerits of AI in UX .

AI and the UX Design Process

AI can lend UX professionals a helping hand during different phases of the design process. Identify pain points or areas that need improvement in your existing workflow. Use these questions understand more about where you can integrate AI:

• What are my current roadblocks?
• What tasks need optimization?
• What’s the expected outcome?

Check out our guide on how to use AI every stage of the UX process .

Designer Francois Bouniq-Mercier created this stunning visualization of the design process.

This gorgeous graphic showcases a classic UX design process based on design thinking principles. (As we continue, you’ll notice we’ve used it to identify the stages ripe for AI use.)

Researchers at Linköping University in Sweden investigated how to augment UX research and design with AI . They interviewed several UX professionals to find out how they were incorporating AI into their workflow.

Participants used generative AI tools like Midjourney, ChatGPT and Dall-E. 

Some queried the tools, looking for inspiration as they began the creative process. Others used it for benchmarking, editing color palettes and changing UI elements.

A general consensus among interviewees was AI will increasingly become part of their workflow. Output they received was of high quality and required very minor manual changes. Tools like Midjourney allow designers who aren’t experts in 3-D modeling to quickly iterate on designs. AI may even start to make design recommendations after reviewing final prototypes and user behavior.

It’s all about finding an AI tool that complements your existing processes .

Choosing the Right AI Tools for UX

Since all the hype surrounding AI from ChatGPT’s release, companies are racing to roll out new AI functionality. Be wary of tools slapping on “AI” in their marketing just for kicks (and clicks).

Features and functionality aside, consider these important factors when comparing AI tools:

• Scalability
• User Friendliness
• Integrations

Don’t forget to pay heed to these important considerations before diving into a comparative analysis of the tools out there:

• Business Goals. What business objectives does the project help satisfy?
• Project Needs . Whether it’s a survey, user testing or data analysis – what does the project (and regular projects) require?
• Features and Compatibility. What features do you need for this and future projects? (More in the section below)
• Training & Support. What training resources and support does the company provide to ensure effective adoption and use of the tool?
• Scalability & Flexibility. Will the tool be able to satisfy not only current needs, but future ones as well? UX Tools must adapt to evolving project needs and growing needs of the UX research process. 
• Secondary Benefits. Look out for versatile AI tools with features that support other areas of the UX process. For example, Marvin acts as an AI research assistant , facilitating data collection and analysis in one place. It also seamlessly integrates with all your existing tools. Two birds with one stone. That’s Marvin.
• Data Privacy Compliance . What regional and international regulations must be adhered to?

Privacy Concerns

Companies constantly recruit participants and users for interviews, focus groups and surveys. It’s their duty to protect user data at all costs . Concealing people’s personally identifiable information (PII) is of utmost importance.

Choose a tool that incorporates these data security measures:

• Data Anonymization – remove PII from any collected or stored data
• Data Encryption – prevent unauthorized access to sensitive information
• Compliance – ensure tools abide by regulations. These include local data protection laws, industry norms and ethical guidelines. 
• Limited Data Collection – minimize unnecessary data collection. Focus on collecting what matters.
• User Consent – choose tools that are transparent with their data usage and security.

Marvin uses advanced privacy filters to blur faces and scrub out any PII from interviews recordings. It’s HIPAA, GDPR and SOC2 compliant, so your user data is always protected.

How to Choose AI Tools to Augment UX Workflows

Choosing from the ever-expanding universe of AI UX Tools can be daunting. Where do you even begin?

In the section above, we focused on what to look out for when comparing tools. Before diving in headfirst, it’s essential to first identify what stage of the design process could use some AI assistance.

Take stock of your current processes to identify weaknesses or inefficiencies. Then use our list below to explore how AI can help rectify them.

Phase I – Discover

Stay up-to-date with the market. Look for tools with data analysis capabilities . Algorithms analyze large and complex datasets accurately and help uncover insights rapidly. Conduct a competitive analysis. Market research tools can scrape and analyze data from competitor websites, social media and customer reviews to identify patterns and industry trends.

Tools can even catch insights that might’ve ordinarily been missed. They also carry out data analysis tasks in a fraction of the time.

Tools: IBM Watson Discovery , Google Trends, SEMRush, Research AI, Q ualtrics , Marvin

Phase II – Define

Researchers or designers could use some help. Let AI tools become your new virtual assistant. Software can now transcribe user interviews as you conduct them, freeing up a designers mindspace for the questions ahead. From summarizing long transcripts to creating notes and synthesizing insights, let your research assistant conduct preliminary analysis for you. 

Tools for Research and Design Assistance: Adobe Sensei, GitHub Copilot, UX Pilot, Stable Diffusion , Marvin

Plus, all that data needs a place to live. Learn more about the universe of research repositories .

Phase III – Ideate

Brainstorm ideas and concepts using content generation tools. Create user journey narratives and maps to understand more about their experiences. Tools use sentiment analysis to analyze social media, forums and feedback to craft detailed user personas. 

Tools: ChatGPT, Blush, Canva, InVideo, TheyDo, QoQo

Use generative AI tools for UX & Product Writing. Populate wireframes with audience specific copy that’s optimized for search engines and different user personas.

Tools for UX Writing: Writer, Copy.ai, Jasper, Content Bot, Grammarly

Phase IV – Prototype

Automate design workflows. AI tools can generate Ul layouts from user requirements and design principles. Generate interactive and realistic wireframes in minutes with prototyping tools. Simply write a prompt and let the tool create several options for you to choose from. Designers can test and iterate on designs much faster with a shorter feedback loop. Save time and effort with these tools. 

Tools for Prototyping: Uizard, InVision Studio, Ando, Midjourney, Framer, Fronty, Visily, Botpress, Prott, Mockplus, Galileo AI, Relume

The above products create wireframes that serve as a foundation for design. You can then edit and tweak elements according to your liking. Tools offer color palette matching, font suggestions and provide access to a large library of icons and logos. They even provide recommendations, helping designers in making informed choices. 

Use AI to create stunning user interfaces. To enhance your UI and branding with AI, use the following:

Tools for UI Elements & Branding: Adobe Sensei, Figma, Canva, Fontjoy, Designhill Al Logo Maker, Khroma, Recraft AI, Sketch2React, Coolors, Colormind, Flair AI, Magician Design, Dall-E 2 , Marvel AI

Phase IV – Evaluate

User research and behavioral analysis tools track usage patterns. They use heatmaps, eye tracking, session recordings, surveys and A/B tests to understand how users interact with Ul designs. Some tools offer predictive insights, using historical data combined with Al training data to simulate user behavior or responses.

Tools that offer user testing automation can expedite different aspects of testing such as sentiment analysis and usability testing. Some tools include AI-powered tools such as card sorting, tree testing and first click testing. These tests provide valuable insight into the user preferences and inform the design process moving forward.

AI tools can also help with design flaw detection. They provide instant feedback on design choices, potential usability issues and accessibility considerations.

Tools for Usability Testing: Maze, Visualeyes, Brainpool, Optimizely, Dscout, UserTesting, Lookback, Hotjar, Attention Insight

List of Top 8 UX AI Tools to Consider

We scoured the web to bring you the top 8 AI UX tools that enhance a designer’s or researcher’s workflow. Each AI tool falls under the stage of the design process it augments.

Discovery Phase

Marvin brings companies’ disparate data into one centralized repository. This makes it easier than ever to collect, organize, analyze and share insights. AI-powered smart workflows let people search across data and find answers in minutes. 

Designers can go as deep or as high-level as they want, while making connections across data sources they would’ve otherwise missed. Marvin’s AI note taker is the first of its kind, and it automatically generates notes from lengthy interviews. This provides a foundation for researchers to build on as they delve deeper into analysis. 

The best part? Marvin integrates seamlessly with applications designers already know and love. Don’t uproot your workflow to accommodate new AI UX Tools — find a product that layers on top of your process and makes it easier to do your job.

All your research in one place. That’s Marvin.

2. Qualtrics

Experience management platform that focuses on customer and employee experience and strategic research. 

Qualtrics is an online tool that enables designers to build, distribute and analyze surveys. This software has powerful AI capabilities besides collecting and processing quantitative data. Algorithms analyze vast amounts of data from chat logs, social media feeds, feedback surveys. They use all this data to generate insights. The AI engine recommends actionable next steps in order to drive tangible business outcomes.

Definition Phase

A ‘UX design companion’ that’s powered by OpenAI (same as ChatGPT). QoQo helps you generate well-rounded user personas from scratch. It builds cards representing each user’s goals, needs, motivations, frustrations and tasks. Map the user journey or get assistance with design briefs and information architecture. QoQo has a Figma plugin, so you can leverage its AI while designing.

It’s important to use QoQo in conjunction with your own user research. It serves as a great AI companion and starting point for user research.

Notable Mentions: NotionAI. Helpful for organizing design ideas and creating documentation for when you begin a project. 

Ideation Phase

If you haven’t already heard of ChatGPT, chances are you’re living under a rock. We like to think of it as a design assistant or intern.

ChatGPT uses Natural Language Processing (NLP) to generate content based on text prompts. Use ChatGPT to outline research plans, create product documentation and user guides. Brainstorm and frame interview questions and generate compelling written copy for your UI. ChatGPT serves as a constant idea generator that can spark creativity among designers. It enables designers to automate mundane tasks and streamline their workflow.

Notable Mentions: Midjourney . Create designs from ideas in minutes using this text-to-image generation tool. 

Prototype Phase

Unanimously the number one choice amongst all prototyping apps. Uizard uses AI to generate wireframes from written prompts. Create prototypes by dragging and dropping UI elements into a design. You can even hand draw a sketch, and Uizard will convert your design into an editable mockup. It even generates code from the sketch to boot.

Use Uizard to iterate endlessly. It creates prototypes based on design best practices. Create aesthetically pleasing and user-friendly designs.

6. Adobe Sensei

Adobe’s suite of applications including Photoshop, Illustrator and InDesign all house Sensei . 

It has numerous AI features and functionality that help automates non-creative tasks. Content-aware fill allows you to quickly replace unwanted objects from an image. Smart object selection enables users to make complex selections with a simple click and drag. Sensei even acts as a design collaborator. Use it to generate alternate layouts or suggest font and color pairings for any design.

Evaluation Phase

7. usertesting.

A one-stop customer experience platform that helps researchers understand their target users. Recruit and onboard study participants on UserTesting . Using AI, conduct a sentiment analysis on large volumes of text extracted from audio, video and other file formats. ML algorithms conduct keyword mapping, by grouping sentiments based on certain words. 

UserTesting’s AI helps identify points of friction in user interactions. Collating data from multiple sessions allows you to visualize the user journey. It generates themes from large volumes of data so you can begin with some preliminary analysis already in place.  

8. Attention Insight

An AI powered tool that provides design analytics. Attention Insight simulates eye-tracking studies and preference tests. Heatmaps and focus maps help identify elements that grab the user’s attention as they navigate through a website or application. 

These insights help uncover usability issues or potential roadblocks in the user interface. Equipped with this information, you can make more informed design decisions. Improve usability, optimize product performance and create more user-centric designs. Track and monitor how product updates enhance conversion rates.

Notable Mentions: VisualEyes. Powered by AI, this application performs similar tasks to Attention Insight.

Best Practices for Integrating AI into UX Workflows

Below are some steps on how to best to incorporate AI into your work:

• Soft Launch – Start Small. Run tests on a smaller, manageable project to test AI’s handling of data. This allows you to identify and iron out any kinks or inefficiencies. Before releasing it across the entire organization, roll out AI tools on a limited scale. This enables an understanding of whether people are receptive to, and will likely adopt the technology. Here are some ideas about scaling research and design operations .
• Data Quality Assurance – Remember, your output is only as good as your input. (We know you’ve heard it before: Bad data in, bad data out.) Focus on good data quality to ensure you’re using datasets that are accurate, complete and consistent . Unbiased and reliable data generates helpful and actionable insights. Set explicit data validation guidelines for data collection to avoid errors and anomalies in the future. Address the quality of your data, don’t neglect it.
• Ensure Human Oversight – Keep user experience in mind throughout the process. Sounds simple enough, but it’s easy to become enamored by the capacity of AI. Researchers and designers can lose sight of who they’re designing for. Don’t fall into the trap. Ensure a varied group of individuals review and test the system before launch. 
• Validate Regularly – Don’t rely solely on AI’s output. Cross-check AI’s findings with human analysis to corroborate insights accurately. 
• Consider Ethical Implications – FACT: AI is trained on biased data. It’s a designer’s duty to ensure that any inherent biases don’t exist in design output. Clearly define the scope of AI used in any project and use it responsibly . Google’s Rida Qadri weighs in on the ethical dilemma facing researchers today .  
• Familiarization – Companies are rolling out new AI capabilities at a rate of knots. Stay up-to-date with the latest trends and future developments in the field. Prioritize continuous learning. We share our thoughts on how to master user research software . 
• Training – Establish best practices for employees at the company to follow. Learning the tool’s functionalities is important, but don’t forget to teach users how to interpret and use AI generated outputs. Educate them on how AI could fit into their workflow . Once they learn the ropes, they can offer feedback for improvements. (Marvin customers do this all the time, and we LOVE them for it!)
• Iterative Methodology – Iterate your work using AI to meet functional and aesthetic needs. Don’t merely accept the first round of AI generated assets. Keep refining the process until it meets your requirements. Create a feedback loop – test wireframes to get quick user feedback and observe where they fall short. If you don’t like something about a certain wireframe, change it. This creates well balanced and effective designs. 
• Collaborate – Constantly communicate with stakeholders, developers and end users. Involve them early in the process. Establish a shared understanding of business goals, the potential benefits and constraints of AI tools. Marrying diverse perspectives and user needs with project objectives leads to a more impactful user experience. Don’t believe us? Learn why industry expert Lou Rosenfeld thinks research can eliminate organizational silos . 

AI and UX: Better Together

AI’s impact on the user experience can’t be understated. We’re only at the beginning of the story. The rate at which AI tools are being rolled out is staggering. AI will only become larger in terms of its significance and reach.

Design’s mandate doesn’t waiver — let’s create experiences that delight users . 

AI will increasingly help us on this path. 

Using AI, experiences can be customized to individual needs and abilities. UX professionals can extract meaningful customer insights from feedback at scale. This improves functionality and aesthetics of the final product or service. It forms intuitive and engaging customer journeys.

AI empowers designers to create engaging products with greater purpose. Unlock greater productivity with AI.

Blog hero image by Pete Wright on Unsplash

Frequently Asked Questions (FAQs)

Now, let’s look at the top FAQs about UX AI tools.

How Do AI Tools Improve User Testing and Data Collection?

UX AI Tools can assimilate large volumes of data from various sources. By automating data collection and processing, a company benefits from efficient data management. Machines analyze data at scale to gauge user behavior and sentiment. AI-enabled analysis doesn’t suffer from design bias and human error.

AI streamlines aspects of user testing. To optimize resource spend, designers can automate aspects of the testing process. UX AI tools can identify potential bottlenecks and usability issues. They can also make suggestions to increase a design’s accessibility. This helps in optimizing design for the prototype stage. A continuous feedback loop facilitates faster product development.

Using AI tools, designers enhance the user experience of a product. It results in them creating more intuitive, personalized and inclusive designs.

Can AI Tools Predict User Behavior in UX Research?

Yes, absolutely! 

UX AI tools use Machine Learning (ML) algorithms to collate and analyze historical data. Algorithms learn from terabytes of datasets to unearth trends and patterns. They use past data to make predictions about future trends and user behavior. 

Advanced algorithms track user behavior and sentiment. They detect patterns in data that help designers create well defined user personas. Designers can customize user journeys based on these personas that AI helped create.

Predictive analytics helps in forecasting user behavior patterns. ML algorithms continuously learn from data. This increases the precision of their predictive abilities over time.

Predictive analytics helps designers identify navigation weaknesses in the user flow. This helps designers adapt the interface for more user-centric design. Designers can make anticipatory changes and feature improvements to create a smoother UI.

How Do AI Tools Handle Qualitative Data in UX Research?

UX researchers conduct user interviews to obtain qualitative data . They gather user testing feedback and ask open-ended questions to do so. Participant responses are often lengthy and complex.

UX AI Tools like Marvin can automatically generate transcripts from interview clips (both audio and video). They store this data in a research repository. 

Once stored, this data must be analyzed. AI tools aggregate thousands of hours of user interviews, to identify common themes. To gauge user delight or frustration, they scan interview transcripts to conduct a sentiment analysis. This serves as a foundational, preliminary analysis before any human intervention. 

AI tools can process large amounts of qualitative data at inhuman speeds. They summarize multiple pages documents within minutes. 

AI tools expedite the time consuming and tedious aspects of handling qualitative data.

Categories:

• Leading the Way
• Marvin Customers
• Marvin Events
• Talk to Your Users
• artificial intelligence
• Customer Research
• User Research

Related Articles

How to Use AI For Market Research (and FAQs)

7 Product Feedback Tools & Software [Explained]

5 Best AI Research Tools (Explained + FAQs)

• Krish Arora
• Latest posts

Krish Arora leverages his experience as a finance professional to turn data into insights. A passionate writer with a strong appreciation for language, Krish crafts compelling stories with numbers and words to elevate the practice of user research.

• How to Conduct Effective Customer Analysis (Tools Included)
• 6 Transcription Software For Qualitative Research
• Your Complete Guide to Product Management Customer Feedback
• (6 Top Picks) Survey Analysis Software - Complete Guide

withemes on instagram

Product Design (UX/UI) Bundle and save

User Research New

Content Design

UX Design Fundamentals

Software and Coding Fundamentals for UX

• UX training for teams
• Hire our alumni
• Student Stories
• State of UX Hiring Report 2024
• Our mission
• Advisory Council

Education for every phase of your UX career

Professional Diploma

Learn the full user experience (UX) process from research to interaction design to prototyping.

Combine the UX Diploma with the UI Certificate to pursue a career as a product designer.

Professional Certificates

Learn how to plan, execute, analyse and communicate user research effectively.

Master content design and UX writing principles, from tone and style to writing for interfaces.

Understand the fundamentals of UI elements and design systems, as well as the role of UI in UX.

Short Courses

Gain a solid foundation in the philosophy, principles and methods of user experience design.

Learn the essentials of software development so you can work more effectively with developers.

Give your team the skills, knowledge and mindset to create great digital products.

Join our hiring programme and access our list of certified professionals.

Learn about our mission to set the global standard in UX education.

Meet our leadership team with UX and education expertise.

Members of the council connect us to the wider UX industry.

Our team are available to answer any of your questions.

Fresh insights from experts, alumni and the wider design community.

Success stories from our course alumni building thriving careers.

Discover a wealth of UX expertise on our YouTube channel.

Latest industry insights. A practical guide to landing a job in UX.

The top 5 AI-powered tools for user research (and how to use them)

Want to supercharge your user research with AI? Discover the 5 best AI tools for user research and learn how to use them for maximum impact.

Free course: Introduction to UX Design

What is UX? Why has it become so important? Could it be a career for you? Learn the answers, and more, with a free 7-lesson video course.

User research is essential for good UX—but it can be time-consuming and resource-heavy. AI-powered tools help to make the process more efficient, getting you to those all-important insights a little faster. 

So what are the best AI tools for user research and how can you use them for maximum impact?

Let’s take a look.

1. Miro Assist

Miro Assist at a glance: 

• Price: You can access Miro Assist with all Miro plans: Free, Starter ($8/member per month), Business ($16/member per month), and Enterprise (custom pricing). 
• Used for: Quickly making sense of your research and ideas in Miro. 
• Learn more: Miro Assist . 

You’re no doubt familiar with Miro, one of the most popular UX design tools on the market. Now you can supercharge your user research efforts in Miro with the help of Miro Assist. 

Miro Assist is a chat-like feature integrated directly into the Miro board itself. It uses machine learning to understand the content on your board, as well as any questions or prompts you enter.

Why is this useful? Well, you can use it to quickly pull out key insights from your board, to condense and summarise information, and to generate new content such as presentations, action lists, and visualisations. Getting to the bottom of your research just got a whole lot easier!

How to use Miro Assist for user research

Here are just a few of the ways Miro Assist can help with user research:

• Summarise ideas: Miro Assist can condense many sticky notes into a single, concise sticky note. This helps to quickly get teams aligned and to define actionable next steps during ideation and prioritisation workshops.
• Generate AI-powered mind-maps and diagrams: Turn unorganised content into meaningful visuals, helping to present ideas and data points in a digestible, easy-to-understand format.
• Cluster sticky notes: When you’ve got lots of qualitative data, analysing it and identifying patterns can be extremely time-consuming. Miro Assist can quickly group sticky notes by sentiment or keywords, helping you to find meaningful trends and create relevant themes.
• Create presentations: You can automatically generate presentations to communicate your research findings and turn them into action items.

[GET CERTIFIED IN USER RESEARCH]

2. Dovetail

Dovetail at a glance: 

• Price: $30 per month for the Starter plan; $375 per month for the Team plan; $1,800 per month for the Business plan. Dovetail also offers a free trial. 
• Used for: Streamlining analysis of qualitative research data. 
• Learn more: Dovetail AI

Dovetail is a user research and customer feedback analysis platform. You can use it to organise and analyse research data, and to foster a more collaborative research process among stakeholders.

Now, like many user research tools, Dovetail incorporates several AI features to streamline the task of analysing qualitative data.

How to use Dovetail AI for user research

Here are some of the most useful AI features available in Dovetail: 

• Sentiment analysis: Speed up the process of analysing quantitative data with Dovetail’s AI-powered sentiment analysis feature. You can use it to automatically identify positive and negative sentiment in your transcripts and notes, to track patterns and trends over time, and to identify recurring pain-points and gains. This gives you rich and ongoing insights into your users’ attitudes and emotions.
• Thematic clustering: Make sense of large volumes of qualitative data by automatically clustering your data highlights into themes. The themes are derived from the content of your highlights, not from tags or titles. Additionally, this feature generates titles for each clustered group—helping to organise interview transcripts and user feedback into coherent themes. This is crucial for understanding what the data is telling you and identifying important action points.
• Auto-summarisation: This feature summarises key points in various types of content, including PDFs and interview transcripts. It automatically generates summaries, allowing you to extract essential insights and takeaways from lengthy interviews, documents, or customer feedback.

Maze at a glance: 

• Price: Free for individuals; $99 per month for the Starter plan; custom pricing for Team and Organization plans. 
• Used for: Generating effective research questions and analysing qualitative data.
• Learn more: Maze AI .

Maze is a firm favourite when it comes to user research tools , and it now comes equipped with a host of AI solutions to speed up your work. With Maze AI, you can enhance various aspects of the research process—from crafting questions to transcribing interviews and analysing data.

How to use Maze AI for user research 

Here’s how you can incorporate Maze AI into your user research process:

• Fine-tune and edit questions: If you want to conduct effective user interviews and surveys, it’s important to ask the right questions. With Maze’s Perfect Question feature, you can leverage the power of AI to identify bias, grammatical errors, and readability issues in your research questions. The tool also offers suggestions for rephrasing your questions. This helps to ensure clarity and neutrality—which is essential if you want to gather reliable data.
• Auto-generate follow-up questions: Leverage AI to trigger contextual follow-up questions based on each participant’s unique responses. This enables researchers to delve deeper into user feedback and gain more nuanced insights.
• Generate themes: Maze’s AI can automatically identify and generate common themes in your open-ended question responses. No need to manually sift through the data to find meaningful patterns—let AI speed up your analysis.
• Transcribe interviews and recordings: Transcribing audio recordings into text is, without doubt, one of the most time-consuming aspects of conducting user research. Fortunately, Maze enables you to automate the process with AI, saving you considerable time (and energy).
• Conduct sentiment analysis: Speed up the process of analysing participant responses with AI-driven sentiment analysis. Maze AI can assign positive, negative, and neutral tags, enabling you to quickly understand your users’ sentiments—and, as a result, the quality of their user experience and overall satisfaction with your product.

Notably at a glance: 

• Price: $25 per month for the Pro plan (includes 30 AI credits); $250 per month for the Teams plan (includes 100 AI credits); custom pricing for the Enterprise plan (includes 250 AI credits). 
• Used for: Summarising research data, conducting sentiment analysis, and data visualisation. 
• Learn more: Notably AI .

Notably is an all-in-one user research platform powered by AI. It offers a range of handy features for making sense of your qualitative research—including video transcription, cluster analysis, and digital sticky notes. With Notably, you can eliminate much of the manual labour associated with user research and focus on the more creative and strategic aspects of your work. 

How to use Notably AI for user research 

Here’s how Notably can assist with user research:

• Generate concise debriefs from interviews: With Notably AI, you can instantly turn hours of interviews into digestible summaries. This enables you to quickly extract participant information, identify emerging patterns, and pull out interesting takeaways—streamlining the oft-messy process of analysing qualitative data. 
• Conduct sentiment analysis: Get to the heart of how your research participants really feel with AI-powered sentiment analysis. Notably can instantly reveal positive and negative sentiments across your entire study, giving you a clear read on your users’ attitudes and emotions.
• Auto-highlight and tag qualitative data: Notably learns how you tag data and makes suggestions for you, improving over time for increasingly faster and more accurate analysis.
• Generate images: Create unique and meaningful visuals to represent your data with the help of Notably AI. Notably can generate images based on the content of your insights. Alternatively, you can describe your vision and let the AI create images based on your input. This is a great antidote to old-fashioned reports and overused stock images.

5. QoQo (Figma plugin)

QoQo at a glance: 

• Price: $7 per month for 1 user and unlimited access. 
• Used for: Enhancing your user research in Figma. 
• Learn more: QoQo .

QoQo is a Figma plugin powered by OpenAI’s GPT (the same technology behind ChatGPT). As such, the tool acts on what it’s learnt from the internet—which means it’s inherently biased. OpenAI has integrated de-biasing models to mitigate this, but it’s important to be conscious when using it (as with any form of AI).

How to use QoQo for user research

Combined with Figma, QoQo is a powerful addition to your tool stack. You can use it to:

• Create user journey maps: User journey maps are a great tool for visualising how users interact with your product. With QoQo, you can speed up the process of creating journey maps, allowing you to quickly step into your users’ shoes and see things from their perspective.
• Create affinity diagrams: Affinity diagramming is a popular method for sorting through dense research data, but it can be time-consuming. QoQo accelerates the process, enabling you to organise your data in seconds.
• Write interview scripts: If you want a faster approach to crafting interview questions, use QoQo to automatically generate scripts. Be sure to tweak them so they sound suitably human, though! 
• Generate user personas: Based on your input, QoQo generates cards that you can use to create effective user personas . Bear in mind that all user personas should be based on user research—not on fictional qualities or assumptions.

Special mention: Looppanel

Looppanel at a glance

• Price: $30 per month for the Solo plan; $350 per month for the Team plan; $1000 per month for the Business plan. Looppanel also offers a 15-day free trial. 
• Used for: Speeding up research analysis, and a research repository tool
• Learn more: Looppanel

Looppanel is an AI analysis and repository tool that supports live user research by helping to synthesize data faster, with efficiency.

Looppanel uses AI to generate call transcripts with over 95% accuracy, create notes on user interviews, do sentiment analysis, and organize bookmarks and themes automatically.

How to use Looppanel for user research

Here are some of the most useful AI-powered features available with Looppanel: 

• Automatic note-taking: Looppanel can join you on user interviews and take accurate notes like a human assistant, leaving you to focus on the conversation. It automatically highlights where questions were answered, and summarizes notes in a Q&A or theme based format. Instead of reading everything from scratch by yourself, you just need to review the AI notes, and add additional context or tags if needed. It can immensely speed up the research process, make sure you don’t miss anything, and reduce your dependence on other team members.
• Repository search: Looppanel’s AI-powered search can look through all your notes and transcripts in the repository to find relevant data, in seconds. You don’t have to spend too much time on repository maintenance thanks to this feature, it works even if you don’t tag the data. With simple Google-like search for any term, concept, user quote, data point, or feature name, you can easily find all the notes and transcript text associated with it.
• AI-powered transcription: Looppanel can provide transcripts of call recordings in mere minutes, with over 95% accuracy. These AI-generated transcripts also highlight important sections and provide auto-generated summaries of each section, to make it easier to analyse. Currently the tool supports transcription in English and 8 other languages.
• Sentiment analysis: Looppanel’s transcription also features sentiment analysis, and identifies questions, positive and negative sentiments in transcripts. This makes it much easier to review data, and save recurring user issues, patterns and themes.

In summary: the role of AI in user research

With the help of the tools we’ve listed, you can automate, streamline, and enhance various aspects of the research process—but nothing can replace your role in conducting and making sense of effective UX research.

Consider AI your helpful research assistant; a powerful tool you can combine with your uniquely human skills, intelligence, and creativity to better understand your target users and deliver outstanding products and services.

Learn more about how to leverage AI for effective user research

If you’re looking for a practical education in leveraging AI for research, check out The UX Design Institute’s Professional Certificate in User Research . You’ll not only master essential skills for planning, conducting, analysing, and communicating effective user research; you’ll also complete an entire module dedicated to using AI throughout the research process. 

Want to learn more about the role of AI in UX? We think you’ll enjoy these posts:

AI for UX: 5 ways you can use AI to be a better UX designer

• Will AI replace UX designers? An honest answer
• How AI will impact UX design: An interview with Nick Babich, Principal UX Designer at Brain Technologies
• user research

Subscribe to our newsletter

Get the best UX insights and career advice direct to your inbox each month.

Thanks for subscribing to our newsletter

You'll now get the best career advice, industry insights and UX community content, direct to your inbox every month.

Upcoming courses

Professional diploma in ux design.

Learn the full UX process, from research to design to prototyping.

Professional Certificate in UI Design

Master key concepts and techniques of UI design.

Certificate in Software and Coding Fundamentals for UX

Collaborate effectively with software developers.

Certificate in UX Design Fundamentals

Get a comprehensive introduction to UX design.

Professional Certificate in Content Design

Learn the skills you need to start a career in content design.

Professional Certificate in User Research

Master the research skills that make UX professionals so valuable.

Upcoming course

Build your UX career with a globally-recognised, industry-approved certification. Get the mindset, the skills and the confidence of UX designers.

You may also like

The best online survey tools to use in 2024

How to find UX researcher jobs in 2024

Build your UX career with a globally recognised, industry-approved qualification. Get the mindset, the confidence and the skills that make UX designers so valuable.

8 October 2024

• UX Consulting
• Industry Spotlight
• Competitor Differentiation
• Moderated Testing
• Unmoderated Testing
• Prototype Testing
• Quantitative Testing
• Qualitative Testing
• Website Usability Testing
• Mobile App Testing
• Multi-Channel Testing
• Branching Logic
• Participant Recruitment
• Private Label
• Accessibility Testing
• Think Aloud
• Content Testing
• Information Architecture
• Surveys & Quant Usability Testing
• Advanced Methodologies
• X-Second Test
• First Click Testing
• AI UX Analysis
• ULX Benchmarking Score
• Sentiment Analysis Tools
• Video Analysis Tools
• System Usability Scale
• Case Studies
• Whitepapers
• Press Releases
• FAQ Clients
• Help Clients
• FAQ Testers
• Help Testers
• Get Paid to Test
• > Client Login
• > Tester Login

AI for UX Research: How Artificial Intelligence is Changing the Design Game

AI for UX Research

User Experience (UX) has become a critical differentiating factor for businesses. To meet users’ evolving expectations, UX research plays a pivotal role in understanding user behavior, pain points, and preferences.

In this context, with the rapid advancements in Artificial Intelligence (AI), UX research has undergone a transformative revolution, enabling UX researchers to delve deeper into analyzing data, streamline processes, and deliver enhanced user experiences.

So, how is Artificial Intelligence changing the UX landscape in terms of design? How can UX researchers leverage this technology —and others like machine learning and data science— to design and develop user-centered digital experiences that have value for companies and clients?

What is Artificial Intelligence — an Expert Definition

According to  TechTarget.com , AI, or artificial intelligence, is the simulation of human intelligence processes applied to machines, usually computer systems.

Basically,   AI tries to make computers able to do the things human minds can . Most AI systems work by taking in large amounts of “training” data, learning to recognize patterns from this data, then applying these patterns to make predictions in real-world settings.

Artificial intelligence (AI) refers to the field of computer science that focuses on the creation and development of intelligent machines capable of performing tasks that would typically require human intelligence.

AI encompasses various subfields, such as machine learning, natural language processing, computer vision, robotics, and expert systems.

The goal of AI is to enable machines to simulate human cognitive processes, such as learning, reasoning, problem-solving, perception, and decision-making.

AI systems are designed for analyzing data in huge amounts, extracting meaningful patterns and insights, and making autonomous or semi-autonomous decisions based on available information.

The subsets and applications of Artificial Intelligence

Machine learning, a prominent subset of AI, involves training algorithms with data to recognize patterns and make predictions or take actions without explicit programming.

Deep learning, a specific type of machine learning, utilizes artificial neural networks inspired by the human brain’s structure and function to achieve higher levels of accuracy in tasks like image and speech recognition.

AI technologies have found applications in numerous fields, including healthcare, finance, transportation, customer service, manufacturing, and entertainment.

Some examples of AI applications include virtual assistants (e.g., Siri, Alexa), recommendation systems (e.g., Netflix, Amazon), autonomous vehicles, image recognition systems, and medical diagnostic tools.

It’s important to note that AI is a rapidly evolving field, and its capabilities and applications continue to expand as researchers and developers make advancements in technology.

Why and How is Artificial Intelligence being used in UX?

In the context of User Experience, AI is slowly becoming more popular  as a supplementary tool  used in research and design phases.

While AI has the potential to be used in creative processes, designers have mainly used the tool to  increase automation, productivity, and personalization of user experiences .

The use of AI amongst teams can also promote collaboration and communication, enrich research development, and improve design efficiency.

Automated user testing

AI can automate various aspects of user testing, making it more efficient and scalable. For example, AI-powered user testing tools can conduct usability tests, eye-tracking studies, and sentiment analysis without the need for human intervention. This helps UX researchers gather customer feedback quickly and at a larger scale.

AI-powered user testing can quickly analyze large volumes of user data, enabling faster testing cycles and accelerating product development . It eliminates the need for manual data processing, saving time and effort.

Further, traditional user testing has some pain points such as recruiting participants, setting up physical testing environments, and hiring human moderators. AI-based testing reduces these expenses by automating various aspects of the process, making it more cost-effective in the long run.

More importantly, AI algorithms can provide unbiased and consistent analysis of user behavior and feedback. By removing human bias, AI helps ensure that user testing results are based on objective data, leading to more reliable insights and actionable recommendations.

Data collection and analysis

AI has the ability to analyze large data sets faster than humans, allowing data analysis from millions of sets to uncover more insightful and in-depth conclusions.

For example, AI could be used to analyze hundreds, thousands, or millions of qualitative and quantitative user experience studies and discover patterns and insights more quickly than a human UX researcher could.

Through AI-powered analytics tools, UX researchers can gather insights from various sources, such as user interactions, feedback, social media, and more. This allows for a deeper understanding of user behavior, preferences, and needs.

In terms of design, using AI to analyze large quantities of data reduces human error and saves time, letting designers concentrate on creating more personalized interfaces. A painless, personalized experience equals greater customer loyalty and willingness to recommend.

Customer Segmentation

As mentioned above, using AI can dramatically improve UX personalization. This provides companies the opportunity to get ahead of the competition, as users will flock toward companies they feel a closer connection with.

Data analysis through AI is facilitating a more precise customer segmentation, giving UX designers more insights to work with. This, ultimately, gives user research specialists the ability to provide a more personalized experience to more people.

AI enables personalized user experiences by leveraging user data and behavioral patterns. UX research process via AI algorithms allows for increasingly granular analysis of user preferences, and tailors interfaces to individual users, leading to more engaging and relevant experiences.

AI-driven recommendation systems also enhance personalization by suggesting content or products based on user behavior and preferences.

Visualizations 

AI can optimize the design process by quickly creating visualizations, and even wireframes, from designer ideas. Using interfaces such as  DALLE2 , designers can either articulate or submit rough sketches of their ideas and quickly be given preliminary designs or wireframes in return.

Designers can also use AI to leverage historical data in order to quickly create user flowcharts. This capability of AI in UX will further streamline the design process, allowing more time to be spent on creative materials. 

Task Automation

In the coming years, expect a surge in the number of companies using AI in such capacities previously mentioned. The automation of repetitive or tedious tasks optimizes the research and design phases of UX development and makes a more personalized experience possible.

When UX designers can allocate tedious tasks to AI, such as data analysis, or using Adobe features to resize and crop images, it frees up time to focus on the creation and personalization of additional interfaces, as well as promoting a more efficient process overall.

From a UX research perspective, leveraging AI to identify patterns and key “Moments of Interest” (MOI) can allow researchers to quickly sift through the increasingly massive amounts of qualitative and quantitative data being generated by advanced remote UX research tools and platforms.

Sentiment Analysis

UX researchers are increasingly leveraging AI techniques to perform sentiment analysis in user research. Sentiment analysis involves analyzing text or other forms of user-generated content to determine the sentiment or emotional tone expressed by users.

In this context, Natural Language Processing (NLP) —a branch of AI— focuses on understanding and processing human language.

UX researchers are employing NLP algorithms to extract meaning from user-generated text, such as survey responses, product reviews, social media posts, and customer support interactions.

NLP techniques help identify sentiment-bearing words, phrases, and contextual cues that indicate positive, negative, or neutral sentiments.

Machine Learning (ML) classification is another AI subset that offers sentiment analysis capabilities. ML algorithms can be trained to classify user sentiment based on labeled data.

UX researchers curate datasets with examples of positive, negative, and neutral sentiments, and then train ML models to automatically classify new text inputs. This enables automated sentiment analysis of large volumes of user feedback.

Emotion Detection

AI models are developed to not only identify sentiment but also detect specific emotions expressed by users. By training ML models on annotated datasets containing emotion-labeled text, UX researchers can uncover emotions like joy, anger, sadness, and surprise.

Emotion detection provides deeper insights into user experiences and helps identify emotional triggers related to specific product features or interactions.

Social Media Monitoring

AI-powered tools are employed to monitor social media platforms and analyze user sentiment toward a product, brand, or specific topics.

These tools employ sentiment analysis algorithms to categorize social media posts and comments as positive, negative, or neutral. UX researchers can then gain valuable insights into public opinion, brand perception, and emerging trends.

Voice and Speech Analysis

AI technologies are utilized to analyze spoken feedback or recordings from user testing sessions. Speech recognition algorithms transcribe audio data, and sentiment analysis models can then be applied to interpret the sentiment expressed in users’ voices.

This approach helps researchers understand emotional reactions in real time, providing additional insights into user experiences.

Image and Video Analysis: AI techniques can be extended to sentiment analysis of visual content. Computer vision algorithms are employed to analyze images and videos, detecting facial expressions, body language, and other visual cues.

This analysis helps identify users’ emotions, reactions, and sentiments towards specific visual elements or design features.

Contextual Analysis

AI-powered sentiment analysis takes into account the contextual factors that influence sentiment. This includes analyzing user demographics, location, social networks, and other relevant information. By considering contextual factors, UX researchers gain a more nuanced understanding of sentiment and can identify patterns across user segments.

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

“auto pilot” user research .

AI has the potential to drastically change how we conduct research. With the automatization of research, UX researchers can still control the questions, but will be free from the task of actually conducting the fieldwork.

The use of AI in this capacity can vastly augment sample sizes and the number of studies conducted, providing quantities and varieties of data that will be far superior to what we currently have, all while still cutting down on research time. 

Creative Uses 

AI also has the potential to become a creative asset for UX designers. We already see AI being used in such capacities on social media, mainly through filters.

When questioned in a 2018 study regarding AI and UX, designers maintained that they wouldn’t rely solely on AI for creative materials, but would be willing to collaborate with AI systems in such a capacity when stuck, kind of like a virtual coworker.

For now, AI will remain a revolutionary tool in the functional operations of UX design while the creative potential of AI is further explored and understood. 

Big Picture

AI has the potential to revolutionize the UX research and design process. This technology could become so widely used, due to its speed and analysis capabilities, that in the future, UX designers will potentially only have to focus on creative materials.

Since UX researchers will be able to manage much larger data sets in an efficient manner, AI could also take UX design from fitting generic, larger consumer groups to designing UX interfaces specifically for individual customers.

Cautions/AI Best Practices

Due to the nature of AI, many people, especially consumers, will be wary regarding the use of such technology. Here are some cautions/AI best practices to keep in mind if you’re going to explore UX research and design with AI. 

Establish trust through transparency

The best way to maintain consumer trust is to keep them informed. Publish educational materials on your website, and send email announcements about how your company will be adopting AI and what exactly that means for them.

Do your best to make sure your customers know what exactly AI is and how you use it.

Maintain Integrity

Many people are skeptical of AI and data collection, so emphasize to your customers that their data isn’t being used in a compromising way.

Also, always be able to explain exactly how and why you use AI so you can effectively answer any customer concerns that arise. 

Don’t Overuse AI

Once AI becomes more widely accessible and used, there may be a desire to think of it as a ‘magic marker’ that will help you solve all your research and design problems. AI, just like anything, is great in moderation. For every project, ask yourself if AI needs to be used and what it would add to the project. 

After reading this, as a designer or user experience researcher you may be worried about AI taking over your job. No need to!

While this technology will be revolutionary in an operational and functional sense to explore larger data sets and automate tedious tasks,  AI does not (yet) have the ability to think freely or solve complex design problems  as easily as humans can. 

However, the introduction and acceptance of AI in UX could change the role of designers in the future.

Designers could potentially change from the role of creator to collaborator as AI learns to automate the more complex steps of the UX research and design process. Designers could potentially use AI to help them with larger, more complex website architectures and code.

AI could even fully automate the process so that designers only have to worry about perfecting the creative work.

And for UX researchers, the sometimes tedious tasks of watching every video session and identifying MOI and patterns, and collating with quantitative data can be replaced with managing the AI systems and study designs, as well as tweaking the follow on studies based on the iterative results delivered by AI analysis.

AI is currently changing the UX research and design landscape through automation and personalization.  As this technology becomes more accessible and widely used, we could see AI taking over larger chunks of the process.

That said, although AI may reduce human error when dealing with large amounts of data and allow for massive scaling of both design and user research, a personalized, human touch will always be needed to manage a project successfully . 

Will AI Replace UX Designers?

Short Answer: No. AI is a powerful tool that can assist UX designers but is not likely to replace them. AI excels at automating repetitive tasks, analyzing large datasets, and providing initial design suggestions. However, core elements of UX design such as empathy, creativity, and the ability to interpret nuanced user feedback remain uniquely human skills that AI cannot fully replicate .

Is There a Future for UX Designers?

Short Answer: Yes. The future of UX design is bright, especially for those who can effectively integrate AI tools into their workflows. UX designers will continue to be essential for their ability to understand and interpret human emotions, create user-centric designs, and ensure ethical considerations are met. AI will enhance, not replace, the capabilities of UX designers, allowing them to focus more on creative and strategic tasks .

Will UX Jobs Be Automated?

Short Answer: Partially. Certain aspects of UX design, such as data analysis, prototyping, and some routine design tasks, can be automated by AI. However, tasks that require human intuition, empathy, and interaction with stakeholders are less likely to be automated. Therefore, while AI will change the nature of UX jobs, it will not eliminate them .

Can AI Take Over Designers?

Short Answer: No. AI can assist designers by speeding up processes and providing data-driven insights, but it cannot take over the entire design process. The creative and empathetic aspects of design, which involve understanding user needs and crafting experiences that resonate emotionally, are beyond the current capabilities of AI .

How Can AI Help with UX Research?

Short Answer: Significantly. AI can streamline UX research by analyzing large volumes of user data, identifying patterns, and generating insights quickly. AI tools can help with tasks such as user feedback analysis, behavior prediction, and data-driven personalization. However, human researchers are still crucial for interpreting qualitative data and ensuring research findings are contextually and ethically sound .

Will AI Replace User Researchers?

Short Answer: No. AI will not replace user researchers but will augment their capabilities. AI can handle large-scale data analysis and provide initial insights, but the interpretation of complex human behaviors, emotional nuances, and ethical considerations requires a human touch. User researchers will continue to play a vital role in understanding and addressing user needs .

Is UX Design Future-Proof?

Short Answer: Yes, with Adaptation. While AI will transform certain aspects of UX design, the fundamental principles of UX—user-centricity, accessibility, usability, and consistency—will remain unchanged. UX designers who adapt by learning to use AI tools effectively will find their skills in high demand. The core of UX design, which revolves around understanding and improving the human experience, will always require human insight and creativity .

Can We Use ChatGPT for UX Research?

Short Answer: Yes, to Some Extent. ChatGPT and similar AI tools can assist in UX research by generating research synthesis, drafting interview scripts, and automating routine analysis tasks. However, human oversight is necessary to ensure that the AI-generated outputs are contextually appropriate and ethically sound. The strategic and empathetic aspects of UX research still require human expertise .

Interested in UX Testing?

Data visualizations, about the author: janna hedlund.

Janna joined the team as a copywriter after receiving her masters in Market Research and Consumer Behavior. When she’s not writing about UX, she enjoys reading, playing sudoku, and going on walks with her dogs and a good playlist.

Related posts:

Ux analysis: how to collect and analyze ux data, the 6 best ux testing tools for ux research in 2024, what is content testing and how to do it properly, latest posts, ai in ux: the ultimate guide to improve ux with ai, the state of ux in 2024, why ceos need to care about ux – before it’s too late, didn’t find what you were searching for, ready to elevate your ux game.

Dive into our Resources Hub for a wealth of UX insights and tools, or jumpstart your journey with a free demo today. Discover how Userlytics can transform your user experience strategy!

Table of Contents

7 ways ai is transforming the role of ux designers.

Created on Aug 5, 2024

Level up your UX process

Figma Plugins

©2024 UX Pilot AI by Adam Fard Studio

Integrations

What's new?

In-Product Prompts

Participant Management

Interview Studies

Prototype Testing

Card Sorting

Tree Testing

Live Website Testing

Automated Reports

Templates Gallery

Choose from our library of pre-built mazes to copy, customize, and share with your own users

Browse all templates

Financial Services

Tech & Software

Product Designers

Product Managers

User Researchers

By use case

Concept & Idea Validation

Wireframe & Usability Test

Content & Copy Testing

Feedback & Satisfaction

Content Hub

Educational resources for product, research and design teams

Explore all resources

Question Bank

Maze Research Success Hub

Guides & Reports

Help Center

Future of User Research Report

The Optimal Path Podcast

User Research

Jan 16, 2024

30 UX research conferences you’ll want early-bird tickets to in 2024

Get out your calendar–it's time to explore this year's upcoming UX research conferences.

Ella Webber

If you’re looking to level up your UX skill set, connect with like-minded professionals, and discover emerging trends across the UX industry, then there’s no better place to look than 2024’s UX research conferences.

Featuring an array of keynote speakers, presentations and workshops, there’s a plethora of UX research conferences you’ll want to sign up for this year, so you can arm yourself with valuable UX research knowledge and get the latest scoop on the state of UX research.

The downside is that you (probably) can’t find time to attend them all. With so many conferences announced for 2024, we’ve compiled the top 30 UX research conferences you won’t want to miss—from the big-hitters to the hidden gems.

Join us for Disco Conf 2024

Be part of our global research and discovery conference, happening on October 17. Online-by-design and completely free—all you need is a curious mind and an internet connection.

The 20 top UX research conferences and events in 2024

We’ve found enough UX research conferences to fill your calendar for the whole year, but if you only have time for a few—let’s break down exactly what you can expect from these UX research conferences.

1. The Virtual UX Conference by NNG

The Virtual UX conference by the Nielsen Norman Group (NNG) strives to share expert UX knowledge with both junior professionals and industry leaders around the world. This virtual conference features two-way video conferencing and digital exercises to sharpen your UX skill set in UX design , product development , or research. Attending NNG’s conference also gives you access to an exclusive Slack community of like-minded user experience researchers, as well as an online UX certification.

Date: January 12-26 Location: Online Price: One live course session is $1,144 Speakers include Maddie Brown, User Experience Specialist at Nielsen Norman Group

Register now

2. Axe-con 2024

Axe-con is an event for UX researchers, managers, and designers to discuss accessibility and inclusive design through a range of activities, including interactive workshops and presentations from global tech leaders.

Participants will have access to special insights and case studies from companies leading the movement towards accessible design and products. With 25k user experience professionals registered last year, Axe-con has quickly become one of the largest accessibility conferences in the U.S.

Date: February 20-22 Location: Online Price: Free Speakers include:

• Dr. Rumman Chowdhury, Founder and Co-founder of the nonprofit Human Intelligence
• Jonah Berger, International Bestselling Author
• Squirmy and Grubs, Disabled Rights Advocates

3. ConveyUX 2024

ConveyUX attendees have the choice of either attending in-person or virtually. The conference's main focus is educating professionals on AI and other emerging technologies. Industry experts share their first-hand experience with UX research strategies while also offering participants plenty of time to connect and grow their network.

Date: February 27-29 Location: Seattle, Washington and online Price: $1,195 Speakers include:

• Aleš Holeček, Corp. Vice President, Microsoft
• Dr. Natalie Petouhoff, CEO, Competitive Consulting Advantage
• Luke Wroblewski, Managing Director, Sutter Hill Ventures

4. IUI ACM 2024

ACM’s conference on intelligent user interfaces (IUI) covers two vital topics for UX researchers: artificial intelligence and human-computer interaction. 2024’s theme is resilience. Keynote speakers will be discussing crucial topics like cyber-resilience and even the COVID-19 recovery. While IUI doesn’t address UX research directly, it’s a valuable way for UX research professionals to gain contextual insights into creating intelligent interfaces with the latest technology solutions.

Date: March 18-21 Location: Greenville, South Carolina Price: Not announced, but previous pricing ranged from $450 for early-bird tickets to $1,050 Speakers: Not yet announced

5. UX Copenhagen 2024

UX Copenhagen is a conference focusing on the complete spectrum of human experience. It covers UX research, design, and content—all while addressing crucial issues like sustainable UX practices and climate change. With over 20 speakers, workshops, and interactive sessions, this hybrid event allows UX researchers to gain fresh insight into UX trends while connecting with like-minded professionals.

Date: March 20-21 Location: Copenhagen, Denmark Price: €1,000 Speakers include:

• Anna Ratkai, UX Researcher of Recorded Future
• Ruby Pyror, Strategic Designer and Manager, Founder of rex.inc

6. Advancing Research 2024

Advancing Research, which began as a virtual conference, is now a NY-based event from Rosenfeld Media, where UX professionals can gather in real life and learn about scalable UX research solutions. It features a main conference along with multiple interactive workshops, primarily focused on re-assessing the state of UX and how it can move forward.

Date: March 25-26 Location: New York City Price: $1,795 Speakers: Not announced yet

7. UX Healthcare Europe 2024

UX Healthcare Europe is a multi-series conference taking place in London, Amsterdam, Berlin, and Prague. This conference is all about improving user experiences for one of our most-vital technologies: healthcare systems. Joined by professionals from both the UX and healthcare industries, UX researchers can use UX Healthcare Europe as a platform to share ideas, discuss trends, and develop a way forward for data accessibility. The ultimate goal? Establish and facilitate a new holistic approach towards patient experience.

Date: April 11-12, June 18-19, September 19-20, and September 25-27 Location: London, Amsterdam, Berlin, Prague Price: €810 or £850 depending on location Speakers include:

• Daniel Trattler, Creative Director at Eobiont
• Vitaly Friedman, Creative Lead at Smashing Magazine

8. IAC: Information Architecture Conference

The IAC is the world’s leading conference for information architecture. It’s been running for 25 years and is regularly attended by hundreds of professionals in the fields of information architecture, user experience design, and content strategy. IAC is about promoting conversations and transferring industry knowledge through presentations, panel conversations, and workshops.

Date: April 9-13 Location: Seattle, Washington Price: $550 Speakers include:

• Rebecca Harper, Information Architecture Community Leader
• Linda Ramirez, UX Designer and Researcher

9. UXinsight Festival 2024

UXinsight Festival brings the largest UX research community in one place for interactive conferences, live Q&A sessions, and eye-opening seminars. In this year’s conference, keynote speakers will tackle the changing trends of UX research landscapes while addressing best practices for optimization. While UXinsight is focused mostly on UX research, it’s ultimately a value-packed festival for any professional engaged with user experience—that’s UX managers and designers, too!

Date: April 15-17 Location: Breda, The Netherlands Price: Not announced yet Speakers include Kerin den Bouwmeester, Founder of UXinsight

10. Chi 2024

Organized by the Association of Computing Machinery, CHI 2024 is one of the largest international conferences covering human-computer interaction (HCI). With both virtual and in-person event options, attendees will learn about the latest HCI trends through keynote speakers, panels, and workshops. The conference is a platform for researchers, industry practitioners, and leaders eager to share their knowledge and explore the changing landscape of HCI.

Date: May 11-16 Location: Honolulu, Hawai’i and online Price: Not announced yet Speakers: Not announced yet

11. UXLx: User Experience Lisbon

UXLx User Experience Lisbon starts with three days of interactive workshops, leading up to a main, eye-opening conference committed to delivering industry insights. Participants will learn about UX research trends, content, design, and strategy. With multiple topics, professionals are free to customize their schedules and attend the activity best suited to their interests. This makes UXLx an excellent conference for UX researchers, designers, and managers alike.

Date: May 21-24 Location: Lisbon, Portugal Price: €1245 Speakers include:

• Meghan Casey, Owner at Do Better Content Consulting
• Greg Nudelman, Distinguished Designer at Sumo Logic

12. From Business to Buttons

From Business to Buttons is a conference for professionals passionate about improving user experiences. In 2024, this Stockholm-based conference will cover how design can mitigate risk, ignite change, and improve accessibility in a complex world. It features workshops, Q&A sessions, and panels by leading industry professionals before finishing off with the main conference for both UX researchers and designers.

Date: May 24 Location: Stockholm, Sweden Price: €754 Speakers include Christina Joy Whittaker, Leadership Strategist and TEDx Speaker

13. UX360 Research Summit 2024

UX360 is one of Europe’s most famous virtual UX events. Organized by the Merlien Institute, it’s a perfect event for UX researchers to join and discuss UX research best practices and the latest industry trends. Some of the keynote speakers work in world-famous brands like Microsoft, American Airlines, and Linkedin. The event features interactive panel discussions on UX research insight implementation and also plenty of networking opportunities.

Date: May 16-17 Location: Online Price: €695 Speakers include:

• Javier Bargas-Avila, Director, Google Play UX Research
• Nyssa Packard, Senior Director, Insights (Head of User Research & Data Science) Skyscanner

14. UXPA International

UXPA International attracts UX professionals from around the world for three days of workshops, panels, and discussions. Their keynote speakers will be discussing important industry insights ranging from design to research. It features plenty of opportunities to network and build relationships with other designers, researchers, and product managers.

Date: June 24-27 Location: Fort Lauderdale, Florida Price: Not announced yet Speakers: Not announced yet

15. User Research London 2024

After a break in 2023, User Research London is back in 2024 as a live conference dedicated to user research. In past years, User Research London covered the importance of both creativity and strategy in UX research, as well as how to thrive as a UX research manager, with an amazing lineup of keynote panelists. While not much info has been shared yet, we’re excited to see what’s coming for this event in 2024.

Date: Late June—exact date yet to be announced Location: London Price: Not announced yet Speakers: Not announced yet

16. HCI International

HCI (human-computer interaction) International features multiple topics spanning from AI in research to data analytics, with the choice of joining the in-person conference or opting for online attendance. This is an interdisciplinary conference, with the organizers looking to bridge the gap between linguistics, psychology, and other fields like data analysis. UX researchers will be able to gain valuable insights from other fields, enriching their skill sets, UX reporting, and widening their networks.

Date: June 29-July 4 Location: Washington DC and online Price: $895 Speakers include Vicki Hanson, CEO of the Association for Computing Machinery

17. UX Nordic

UX Nordic connects researchers, designers, and writers from the Nordic region and beyond. It lasts three days and offers innovative workshops, panel discussions, and a main conference to nurture your UX skills regardless of industry experience. UX Nordic also promises an altogether immersive experience with award shows, activity extras, and great food and drink.

Date: August 28-30 Location: Aarhus, Denmark Price: €425 Speakers include:

• Irene Au, Design Partner at Khosla Ventures
• Andy Budd, Design Leadership Coach

18. uxcon vienna

With 30+ international speakers and industry leaders, uxcon vienna is one of Europe’s largest UX conferences. Besides networking opportunities for junior professionals and exposure to the newest UX research methods and technology in the industry, uxcon vienna also connects Europe’s leading UX teams with UX practitioners in the United States. In 2024, uxcon vienna will be focusing on topics like AI in UX design, UX psychology, research ops, and trends to expect from the UX industry.

Date: September 19-20 Location: Vienna, Austria Price: €890 Speakers include:

• Nikki Anderson, Founder of User Research Academy
• Mick Champayne, Senior Visual Designer at Google
• Steve Portigal, Independent Consultant and UX Author

19. UXDX EMEA

UXDX EMEA is a conference seeking to break the barriers between product design, development, and user experience. It’s perfectly suited for UX researchers, managers, and other UX professionals seeking to improve collaboration, efficiency, and sustainability for their UX team. This conference includes over 20 talks and an after-party to network and celebrate the newly gained UX knowledge.

Date: October 9-11 Location: Dublin Price: €999 Speakers: Not announced yet

20. Disco Conf by Maze

Drawing in over 14,000 UX professionals last year, Disco Conf is a discovery, design, and research conference. Keynote speakers from leading companies and industry pioneers will take to the virtual stage to break down changes in UX while giving you the insights you need to maximize product research effectiveness. It’s a completely free and online-by-design event, so you can tune in from wherever you are.

Last year, Disco Conf ‘23 offered a four-track agenda covering everything from AI, democratization and continuous learning, to decision-making, research methods, and accessibility. In 2024, we’re setting the stage yet again with a panel of incredible speakers eager to share their expertise with the global UX community. Register to be the first to hear when we unveil Disco Conf 2024's speaker line-up.

Date: October 17 Location: Global and online-by-design—tune in from anywhere! Price: Free Speakers: To be announced; past guests included speakers from Google, Notion, Miro, Microsoft, Figma, and more

More UX research conferences: 10 bonus UX events to consider in 2024

Still haven’t found the perfect UX research conferences for your 2024? Here are 10 more UX events worth checking out this year:

• HUCCAP 2024 | February 27-29 in Rome, Italy
• DDX 2024 | March 2 in Dubai, UAE
• UXDX USA | May 15-17 in New York, US
• Quant UX Con | June 12-13, online
• Ace! | June 13-14 in Krakow, Poland
• C3 Amsterdam | June 14 in Amsterdam, NL
• Pixel Pioneers | June 14 in Bristol, England
• UX London 2024 | June 18-20 in London, England
• SmashingConf Freiburg | September 9-11 in Freiburg, Germany
• Push UX | November 7-8 in Munich, Germany

Connect with UX professionals at UX research conferences in 2024

With so much going on in UX, 2024 offers a variety of UX conferences you definitely won’t want to miss. Many of the conferences on this list, including Disco Conf by Maze , are online-by-design or offer virtual experiences, so there’s no reason to miss out on those must-watch interactive sessions, panels, and presentations to help craft the perfect user experience.

If you’re going to an in-person conference, make sure to grab early-bird tickets for the best deals. You’ll also need to arrange transport and accommodation if required, so getting ahead of the game will help save you money and hassle. Then, all that’s left is to plan your agenda and count down the days to your next conference. We’ll see you there!

Frequently asked questions about UX research conferences

What is a UX research conference?

A UX research conference is an event where professional UX researchers, designers, and managers gather together to share expertise, latest developments, and insights on the UX field. A UX research conference will typically last more than one day, with various activities such as discussions, presentations, and networking opportunities.

Where are there UX conferences in Europe?

Some UX conferences in Europe include UX Nordic in Aarhus, Denmark, UXLx: User Experience Lisbon in Portugal, and UX360 Research Summit 2024 in Berlin, Germany. Keep in mind that there are also virtual conferences like Maze Disco-Conf that you can access from anywhere in the world.

Are there any free UX research conferences?

Yes! While most UX research conferences charge a price for attendance, there are plenty of free and accessible options, too. Check out Disco Conf by Maze, AXE-CON 2024, and Quant UX Con. However, make sure you register on time, as the number of attendees may be limited.

Plan Your Trip to Tomsk: Best of Tomsk Tourism

Tomsk, Russia

Essential tomsk, things to do.

Places to stay

Food & drink

Tomsk Is Great For

Art & history.

Eat & drink

Concerts & shows

Historic sites

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

A pilot screening of prevalence of atopic states and opisthorchosis and their relationship in people of Tomsk Oblast

2007, Parasitology Research

Related Papers

Public Health Open Access

stephen aremu

Introduction: Opisthorchiasis is no doubt one of the most neglected infectious disease inspite of its huge medical importance in some parts of the World. The past decade have seen a resurgence of interests in research relating to this public health issue, however there is still a lot to be done. Social Model: Not many models have been explored in Western Siberia to deal with the opisthorchiasis epidemic when compared to the different models that have been used for other regions affected by similar disease. Life Cycle: The complex life cycle of Opisthorchis felineus has humans and other feline species as definitive host and is really prevalent among the aboriginal population of the Western Siberian because of their habit of eating raw or undercooked fresh water fish (Cyprinidae) which are intermediate host of the parasite. Diagnosis and Treatment: Diagnosis involve the use of stool microscopy, other methods such as mAb ELISA, LAMP and so on are used, while the common treatment is the...

Charlotte Braun-fahrländer

World Allergy Organization Journal

Maria Prisco

Izabela Kupryś-Lipińska

Introduction: A dramatic increase in the prevalence of atopic diseases can be observed. The reasons for this phenomenon remain unclear. Aim: To compare the prevalence of atopic diseases in subjects living in the city centre and a rural area. Material and methods: The study was done on a randomly chosen group of inhabitants of Lodz province, aged 3 to 80 years, living in two different areas: the city centre and a rural area. Demographic data and the anamnesis were collected on the basis of standardised questionnaires. Additionally, skin prick tests and screening spirometries were performed. Results: The complete data from 482 subjects living in the city centre and 469 in the rural area were included in the analysis. Asthma prevalence in the city centre was estimated at 13.2% in adults and 18.4% in children compared to 4.2 and 6.0% respectively in the rural area. The prevalence of seasonal allergic rhinitis in the city centre was 13.2% in adults and 16.1% in children, in comparison to...

Online journal of biological sciences

Alexandra Tegza

Clinical & Experimental Allergy

Silver Siiak

Journal of Parasitic Diseases

Amin Ahmadi

PLoS Neglected Tropical Diseases

Hafizatul Zan

Suez Canal Veterinary Medical Journal. SCVMJ

Eman Youssef

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Adnan Custovic

Veterinary World

Veterinary World , Dmitry Podshibyakin

Parasitology Research

Menelaos Lefkaditis

The Korean Journal of Parasitology

Dr. Qais Majeed

Nina A Samoylovskaya

International Archives of Allergy and Immunology

Johannes Ring

Georgiana Deak

Heri Wibowo

Pediatric Allergy and Immunology

Helminthologia

Vasile Cozma

Croatian Medical Journal

Lucija Buneta

Veterinary Parasitology: Regional Studies and Reports

Christina Loftin

Journal of Allergy and Clinical Immunology

Laura Rodrigues

Iranian journal of parasitology

Nasser Hajipour

Acta Tropica

Paiboon Sithithaworn

Tropical medicine and health

Kazuhiko Moji

Environment and Pollution

Barbara Kołłątaj

İrfan Şencan

ricardo lopez

Turkish Journal of Veterinary & Animal Sciences

Nurainie Ismail

Elpis Mantadakis

Jane Hoppin

Emerging Infectious …

Teresa Bossù

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024