If you want to improve your research skills by incorporating AI into your research process then you are in the right place. In this article, you will learn:
AI has already claimed its place in UX research. The trend is not expected to slow down in the coming years. To stay or become a world-class UX researcher, you must level up your skills around methods to use AI in research.
One of the most important skills you need to have before implementing AI elements into your research is the professional selection of tools.
Generally speaking, we must differentiate between AI tools and AI-supported research tools. AI tools are often very new and solely evolved around AI functionality. Tools that are only AI-supported on the other hand added AI features in an already existing tool that could work without AI.
Let's start with the more common and widely known tools. There are already a lot of big user research software companies out there that have incorporated AI into their processes. In this group, AI usually plays a minor role and helps with small tasks to make the researcher's life easier.
Currently, these are the safer options for early adoption as you can fall back to old patterns if the AI result isn’t good enough yet.
Here is a list of commonly used tools including their current AI use cases according to a recent study:
The tools mentioned above were already successful without AI. They gradually implement AI elements where it makes sense.
AI tools are often very new and highly innovative tools that depend on creating value through AI features. Therefore, they promise more automation while also increasing the risk of being very dependent on artificial intelligence.
When using those tools you have to conduct thorough research on effectiveness and limitations as many are not good enough to generate insights reliably.
The list of tools I want to mention here is based on adoption to ensure it only contains products that are already in use by professionals:
To choose new AI tools and features that you can trust you need to stay up-to-date with the latest developments in UX research.
The best way to ensure you don’t miss anything is a well-curated set of information sources. Start by subscribing to newsletters from organizations and websites like UX Design Institute and my newsletter where those topics are discussed frequently.
Join communities and forums like Reddit's r/UserExperience to engage in discussions about emerging AI-driven tools.
If you want to work with AI tools it is tempting to just accept that they are doing a part of your job. However, being a clueless user can harm your research results. You don’t have to be a machine learning engineer but some background may help to ensure high-quality output.
If you use AI tools professionally it is crucial to know how it works. This will sharpen your judgment when it comes to your individual use cases.
Regular code works by specific predefined rules. Every rule has to be explicitly written by the developer. Behind artificial intelligence developers work with machine learning algorithms that find patterns in data sources to find their unique set of rules.
Therefore, AI software works for very complex cases but developers give up control over the exact behaviour. AI isn’t trained to think like humans, it just tries to simulate output based on data.
You have to know that:
When evaluating the trustworthiness of AI tools, evaluate factors like the company's reputation, reviews, and case studies. Always test the tool or feature on a small project and analyze the results critically.
“As a rule of thumb, you can trust AI features that assist traditional research methods more often than AI tools that imitate user behavior ”
Staying educated and careful in your approach will ensure you incorporate the most effective AI tools into your UX research projects.
Currently, you should never blindly trust AI to do your research. You still need to be an expert in research to get the most out of the features and ensure you can trust the insights. Therefore, read up on all methods you want to use and become an expert. There are so many research techniques and you will frankly never master them all so focus on those you need regularly and master those.
Especially if there is face-to-face interaction no artificial intelligence will be good enough to replace you anytime soon. So it makes sense to focus on those areas to stay relevant in the future.
There are use cases for almost any research method and if you know how to incorporate AI in some, you can figure out ways to do it in others as well. Hence, we will focus on the most common methods to discuss hands-on applications of AI.
For those two methods, the use cases of AI are similar so we will group them. Luckily, AI can be applied in each of the three stages of an interview or a focus group. Hence, these methods highly benefit from AI usage, especially as they are more time-consuming than other methods.
Recruiting and Screening: A very important foundation for any user interview is to find good research participants. This step can be automated through AI-driven survey tools.
You can simply use a tool like Hotjar Ask to create and screen potential participants. If you manually add a question where they can drop their email if they are willing to be interviewed you have created a recruitment funnel in no time.
Tip: Add a calendly link to automate scheduling entirely.
Communication templating: If you need to email your participants test generation tools like ChatGPT or JasperAI can easily create templates for you. But make sure to add a human touch if the output is quite dry and formal.
Outline generation and check: Finding the right questions to ask is sometimes a real blocker, especially if you are new to interviewing. Text generation tools are not good enough to just use their output without editing, but they help to start your thought processes.
Note-taking or transcribing: Stop transcribing your interviews by hand ever again! There are already great solutions out there that transform any audio into a text-based format. And the best part is, that these tools are highly reliable already. I recommend exploring Otter.ai or Looppanel for this task.
Sentiment Analysis: For any type of user-generated statements you can apply sentiment analysis to cluster or highlight certain emotions behind your users. While ChatGPT can already do a decent job at this it is by far not the best solution. Tools like tl;dv or Dovetail do a way better job at this.
Semantic Analysis: The same can be said for semantic analysis, where text is clustered by context. ChatGPT can do a better job at this but still, UX research tools will be way more helpful if you have the budget.
Tip: If you need help with good ChatGPT analysis prompts, here is a great resource that might help.
Transcribing and video editing: No matter if you do your usability tests remotely or in person as long as you have video or audio you can use a transcription tool as described before to get a text-based version for further analysis.
But there is one addition I would love to make. If you want to share parts of the video with others you can use AI tools to not only transcribe but also cut the videos without much skill. You can use a research tool like WEVO or repurpose a video editing tool like Descript.
Heatmaps: WEVO can also create and simulate heatmaps to test your design against common patterns. Be aware that simulating user behavior is fast but risky, as the simulation is based on potentially biased data.
Question Generation: One of the more obvious applications is question generation. Language models like Jasper AI or ChatGPT can create questions but you still need an expert’s eye on them afterward as they might miss the actual purpose of the question. Common survey tools like Hotjar also offer question creation. These are more tailored models and hence more reliable. Still, they can lead to biased questions and shouldn’t be used without a second thought.
Summarization and Analyzation: If you want to help with this step you are in luck as several products have a analyzation feature. If you want to have a more professional solution you should create your survey with a product that has such a feature. Otherwise, you might need to use a general language model for this.
As we walked through some use cases, it became apparent that AI will transform the process by increasing speed and granularity through automation and prediction. You need to stay critical and make sure to double-check the effectiveness of AI assistance, but it is already a huge asset for any UX researcher.
If you want to automate your research processes start by changing things one by one. While it seems AI is doing all your work you still need to steer the ship in the right direction. Every one of these tools has a learning curve in the sense of how to use it and how much to rely on the findings.
Are you ready to incorporate a new AI tool into your research project? Let me know! I would love to add your experience to help others.
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