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AI in user research

An introduction to AI and how it can be used in user research.

In this video, user researchers discuss AI in user research.

Artificial intelligence (AI) refers to the ability of machines to perform tasks that usually require human intelligence.

This technology has developed and spread rapidly in recent years. No matter your personal opinions of AI, it is undeniable that it is now part of our everyday life – and many people and organisations are keen to further explore what these technologies have to offer us.

What is AI?

You might already be aware of or have used commercial AI tools such as ChatGPT or Grok – but have you heard of “machine learning”, “deep learning”, “computer vision”, “large language model”, or “neural network”? These are all examples of technologies that fall under the umbrella of AI.

If you have not heard of these, do not worry! We’re not going to go into detail about what they are and how they work – this is a course on user research, not software development or computer science.

However, before we can consider how AI can be used in user research, it will be helpful for you to have at least a basic understanding of the kinds of things AI can do. AI technologies can be used to:

generate content such as text, images, and audio
identify patterns and make predictions based on data
interpret and respond to sensory input like speech, images, or movement

These are just a few examples. AI is a broad and fast-evolving field, and many systems combine multiple capabilities. For instance, a tool might generate text based on patterns it has learned from large datasets or interpret spoken language and provide a written summary.

In the AI Playbook for the UK Government, you can find information about the formal categories used to differentiate different types of AI technology. However, you may find it helpful to think about AI technologies in general categories that reflect the the kinds of tasks they support:

Generative AI products new content such as text, images, video, or audio, based on patterns learned from large datasets. It is often used to draft documents, summarise information, or produce creative outputs.
Predictive AI identifies patterns in data to anticipate behaviours or forecast events. Examples include, financial forecasting, personalised advertising, or route planning based on traffic data.
Perceptive AI interprets sensory input from the environment, such as speech, images, or movement. This includes technologies like facial recognition, live translation, and autonomous vehicles.

These categories are illustrative rather than exhaustive, and many AI systems combine elements from more than one area. For a more detailed and policy-aligned overview, refer to the AI Playbook.

AI in user research

User researchers work with a lot of data – interview recordings; transcripts and notes; survey responses; analytics or testing data; participant diary studies; and much more.

Gathering this data is only the first step – much of a user researcher’s time is spent analysing and processing data so that they can communicate useful insights to the wider team.

User researchers work with both quantitative (numbers based) and qualitative (text based) data, which means doing a substantial amount of different types of data analysis. As a user researcher, you will frequently have to:

transcribe and summarise interviews or group discussion sessions
analyse notes from user research sessions, including observation and field studies
review participant diary studies
collate survey and questionnaire responses
interpret analytics, A/B, and usability testing data
identify recurring words, phrases, trends, or themes in user research data
synthesise results, generate insights, and write reports on findings These tasks are time-consuming, and this is where AI tools may be able to help. For example, AI could support you to:
transcribe and summarise large volumes of text
identify recurring topics, quotes, themes, or trends
improve accessibility, for example by providing live captions during online sessions
generate draft documents, including reports, proposals, or privacy statements

It is important to note that while AI can assist with these tasks, it should not replace the critical thinking and contextual understanding that user researchers bring. Some tools may offer features described as “analysis”, but these should be used with care, particularly when working with sensitive or complex data.

In the UK Government, the use of AI is guided by 10 core principles designed to ensure responsible, ethical, and effective deployment. Principle 4 is that you have meaningful human control at the right stages.

In user research, this means that AI tools should support your work, not take over decision-making. User researchers must remain actively involved in interpreting data, drawing conclusions, and making ethical judgements.

Technology evolves – many of us remember a time before smartphones and widespread internet, but the world of work has adapted to these changes. AI is another step in that evolution. It is a powerful and fast-moving technology that can offer real benefits to user researchers, but only if it is used thoughtfully and responsibly.

We will learn about the potential risks of AI in Week 2.