Since I’ve been tinkering with conversational and generative AI models over the last several months, I found myself wondering how these shiny new tools could be incorporated into the user research process. Artificial intelligence is the newest frontier in technology after all, and it’s been making big waves in countless industries, so how could it not have the potential to revolutionise the user experience landscape?
As it turns out, at the intersection of AI and user research, there lie some truly spectacular possibilities – thorough desk research, comprehensive interviews, seamless data cleaning, and rich analysis – so let’s jump in!
- Supercharging your desk research
Personally, I've discovered that an incredibly useful approach is leveraging AI tools during the initial desk research for qualitative studies. These tools really excel when it comes to curating sources, synthesising information from across the internet, brainstorming topics of interest, and formulating hypotheses.
While sifting through the large swaths of data available online, platforms like ChatGPT (a personal favourite), Scite.ai, or SciSpace CoPilot help with literature reviews, provide relevant sources on topics of interest, identify common themes from across sources, and provide useful suggestions on how to proceed forward with the study. I’ve found that this not only saves time, but also significantly expands the depth and breadth of my understanding of the topic at hand by helping me find needles of relevant information in the digital haystack.
- Nailing the interview (or survey!)
Beyond developing hypotheses and defining the direction of research, AI can also help plan and execute user interviews, surveys, or focus groups, through various stages.
You could start by developing themes, topics, and even sample questions for respondents using language-learning models such as ChatGPT. Once you have an idea of what your questionnaire will look like, you could go to one of many survey creation tools out there like Qualtrics, Typeform, and SurveyMonkey, which have their own AI plug-ins. These are designed to develop comprehensive and effective questionnaires, anticipating and accounting for topics of conversation you might miss out on otherwise. Ask Your Target Market (or simply, aytm) can be used to segment your user base, design surveys, collect data, and even organise your findings; while Otter, Notably, or LoopPanel aid in recording, note-taking and transcribing!
- Organising and analysing research data like a pro
When it comes to organising and analysing user research data, efficiency is truly key. AI has been completely reshaping this process, ushering in a new era of speedy and simple cleaning, categorisation, and analysis of data.
A tool that I personally rely on is Dovetail, which can be used to transcribe audios and videos, edit and organise responses, conduct sentiment analysis, and code data based on relevant themes. Other popular tools out there are MonkeyLearn – which swiftly dissects open-ended responses, identifying sentiment shifts and prevalent themes – while Delvv.io segments study participants based on their inputs, and Trifacta automates data cleaning ahead of analysis. Remesh, Speak.AI, HeyMarvin, and Notably are all heavy-hitters when it comes to analysing user research data in various formats - textual, audio, video, and so on.
Extracting meaning from a sea of information is no longer a painstaking process. With AI by your side, it can be really satisfying, accessible, and of course, efficient! But it’s important to rely on a mix of your own expertise and AI’s dynamism to strike that perfect balance in your user research endeavours. Think of AI as an assistant, something to amplify your own research prowess and capabilities, not supplant them entirely.
Cover photo by ThisIsEngineering
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