5 Emerging trends in AI-powered user research

For most user research teams, it takes days or even weeks to create and gather the necessary responses for a research study.

Once the responses are in, it often takes another few weeks buried under piles of survey responses and interview notes to make sense of what insights are hidden in the data.

Let’s face it: traditional user research workflows are slower and more tedious than what they should be.

But a lot of this is changing as we embrace AI in user research. AI tools can make your UX research processes a breeze, from creating research plans and conducting moderated interviews to sentiment analysis.

Let's break down the five biggest trends in the AI-driven user research process.

5 key trends in AI-driven UX research to look out for

Here are the top five trends in AI-driven user research based on our observations in the world of UX.

1. AI-moderated research studies

Research suggests that a user research project takes 42 days on average, while a product discovery project can take as long as 60 days.

Lengthy project timelines have long been a headache for user research teams. They’re constantly struggling to manage the many moving parts of a research project, from getting approvals and managing the scope to recruiting participants and analyzing results.

This is where AI tools like Wondering can change the game for UX research. With Wondering’s AI-moderated research, you can roll out a new research study in minutes and achieve a 16x faster time-to-insight.

All it takes is a prompt explaining the essence or goal of your research project. Then, Wondering automatically generates a full-fledged research study with different quantitative and qualitative questions related to your objective.

You can quickly customize this study and make necessary adjustments before rolling it out to your audience. Or, start from scratch and build a study based on your needs.

Besides making it a breeze to create a research study, Wondering also enables UX teams to conduct multilingual user research and reach users across different parts of the world.

You can create a study in your preferred language. Then, allow end-users to answer questions in their preferred language. When analyzing results, Wondering will show results in your original language.

2. Automated data analysis

Data is at the heart of UX decision-making. But analyzing data can be daunting.

You often handle a large volume of data, nuanced responses, and contradicting points. The result? Days of back-breaking analysis with a high scope of error.

This is another area where AI technology has positively impacted user research. You can use AI tools to analyze massive datasets much faster than humans and produce insights with greater accuracy.

Plus, AI tools can analyze both quantitative and qualitative data.

Quantitative data: Review and organize research responses at any scale and discover key trends or themes.
Qualitative data: Find rich insights by understanding user sentiments and extracting meaningful quotes or insights to support your hypotheses.

AI-powered data analytics tools like Wondering process research data in real-time to shorten the time between data collection and insight generation.

The platform automatically analyzes data as soon as you receive responses to your research survey or interview. For each study, you can see the results dashboard, documenting data points like:

Overall summary for each question
Key themes identified in user responses
Supporting quotes and notes for each theme

You can also go through individual responses to get more granular insights at a user level. This will also give you a transcript of each user’s response in an AI-moderated interview.

3. Generating research plans

A research plan prepares the groundwork for any project by outlining its goals, methodology, tasks, timeline, participant profiles, and more. Building a research plan is naturally time-consuming, and there are so many elements involved.

Another emerging trend is UX teams’ increasing reliance on AI tools to create a research plan quickly rather than spending hours writing one from scratch.

Maria Rosala, the Director of Research over with our friends at the Nielsen Norman Group, shares her step-by-step process and prompts for creating a research plan with any AI tool like ChatGPT.

Let’s break down these steps:

Provide context: Ideally, you want to share enough context about your company, audience, and research project. You should also set the right expectations about your research goals. This gives the AI tool a clear direction of where you’re headed.
Generate questions: Once you’ve set the stage to generate a research plan, start by asking for a list of questions to include in your study. Selectively choose questions that make sense to your research.
Finalize methods: Based on your chosen questions, you can also ask the ideal methodology to conduct this research study. This is where AI tools can help you visualize different versions of your study using various methods.
Find inclusion criteria: You can also ask AI tools to list down the criteria for recruiting participants. Use these parameters when finding candidates or add them as screening questions in your study.
Create necessary collateral: Lastly, you can also generate collateral for your research project, like email copies.

Remember that each step will require a few iterations till you can find the best results for your research plan.

4. Automation in the design process

One of the most rewarding trends in AI-driven user research is automating multiple workflows to fast-track the design process. These tools can perform several tasks with higher efficiency and free up human researchers’ time to focus on high-value tasks.

You can use AI to:

Analyze survey or interview responses
Conduct usability tests and map interactions
Transcribe interviews into different languages
Track and analyze user behavior across their journey
Generate wireframes based on specific guidelines and goals
Create research reports and visualize data to share insights

AI tools can streamline your research and design process, leading to greater productivity and faster shipping timelines.

5. Overcoming research biases

Bias can compromise the integrity of any research project. With AI tools, UX teams can process data with higher objectivity and avoid researcher bias.

Large language models can analyze user responses and study behavioral cues without the cognitive biases of a human researcher. This unbiased analysis can lead to a more accurate understanding of your users.

You can also use AI to review the questions in your research study or interview script and identify potential bias. This allows you to reframe leading questions and use more objective wording.

Besides, you can also train your chosen AI tools on diverse and inclusive datasets to prevent biases. A continuous training effort can ensure that these tools don’t develop biases while analyzing data, and present accurate results.

Embrace AI to level up your user research efforts

Running effective user research projects doesn’t have to be ridden with challenges. AI tools can do a lot of heavy lifting on your behalf to simplify your research efforts and get closer to your users.

So, create your plan of action to embrace these key trends in AI-driven user research. If you want to get started with Wondering to launch AI-moderated research studies and level up with AI-powered data analysis, sign up for free here!

Author

Axel Thomson

Founder & CEO

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