Revolutionising User Testing: How AI will transform the way we evaluate user experience

While there are many online debates about whether AI will replace human jobs, there is also a number of people who are keen and excited to know how AI can assist and support technology in our daily lives.

If you’ve ever wondered about the potential for better user testing, you’ll find this article of interest.

Let us get started by examining the current issues.

User testing has become a standard part of the design process in the tech industry to better solve user problems and provide them with what they need. Although our current user testing tools provide us with the necessary features to conduct tests — such as a testing platform, assistance with user recruitment, test planning and design — there are still some areas where improvement is needed to streamline the user testing process:

• Recruiting moderators and users is a time-consuming task.
• Sample sizes can be limited and small, particularly if language restrictions apply.
• Testing tools can be expensive, making them inaccessible to small businesses and startups.
• Manually analyzing user feedback and behavioural data is a laborious process.
• Manual analysis may fail to capture subtle user emotions and eye movements.
• Setting up and configuring tests manually is prone to errors and time-consuming.
• Current tools lack integration with other platforms, among other issues.
  and many more…

So, how will AI help with all of these points?

Envisioning User testing through AI- Powered User testing platform:

1. Defining Objectives:
We begin any testing by first defining objectives and usability issues and determining what we want to achieve before we start and plan the test.
AI tools can help designers identify usability issues and define their testing objectives.

However, it should be noted that there is an ongoing debate regarding the effectiveness of AI in this area.

AI can try to use several algorithms and find optimal, applicable to those big data. It can also be asked to learn and adapt algorithms. But it cannot set a correct objective when a person is unable to do it.

- Yegorov, Yuri. (2018). Re: Does the Artificial Intelligence is the best for setting research objectives?

2. Test Plan Creation:
The beginning of user testing often requires a lot of time and effort.
AI tools can help designers by automating many of these processes, including test plan creation, script generation, and preparation of material for research.

Here is one example of an interaction with an AI tool about concluding a study with suggestions of some open-ended questions for the participant, and rating scales intended to assess the overall experience.

Source: https://uxdesign.cc/can-chatgpt-write-a-proper-usability-test-script-f8f6f89b5a2e

3. Recruiting Participants:
The next step is to recruit participants who represent the target user group more accurately.
By analysing demographic, psychographic, and behavioural characteristics, AI algorithms can identify users who are more likely to be interested in participating in the study. This will result in reducing the time and effort required for user recruitment while also providing a more accurate representation of the target user group.

4. Conducting the Test:
During a test, we typically ask users questions to understand their needs and observe them using a product or service to perform specific tasks.
Here, with the help of AI, we can convert text into voiceovers that can be incorporated into AI avatars.

Source: https://www.synthesia.io/

This approach offers several benefits, such as :

• Saving time and money by eliminating the need to hire human moderators.
• AI-driven user testing can also assist users in setting up the device and the testing process, thereby automating moderation.
• Additionally, localising the test becomes easier since creating an AI moderator allows us to target users speaking different languages.

Source: https://solidusertests.com/

5. Insights and feedback:

Once the testing is completed, we collect quantitative data that includes task completion rates, participant feedback, and more.
This data is then analysed to identify areas for improvement by identifying pain points, patterns, and opportunities.
AI can play a role in this step in several ways.

• It can generate transcripts, notes, timestamps, and highlights of user feedback and experiences, as well as identify the sentiments in sessions.

Source: https://www.looppanel.com/

Source: https://www.usertesting.com/

• Emotion recognition through speech and facial expressions, as well as emotion heat maps, can be utilised for emotion analysis.

Source: https://www.odaptos.com/

• Eye movement patterns and heat maps can be traced to determine where people concentrated their attention.

Source: https://www.wevoconversion.com/

In conclusion, AI is set to revolutionise the way we evaluate user experience. With the help of AI tools, UX designers can save time, effort, and money while gaining more accurate insights into user behaviour and needs.

It’s not a question of AI replacing skilled humans, because AI can serve as a tool for augmenting the human intellect, along the lines originally envisioned by Doug Engelbart as his goal for advanced user interfaces.

However, it’s important that AI-produced results still need to be carefully reviewed and interpreted by designers to ensure they are accurate and useful.

Extra examples:

• Sentiment analysis
• Tracing eye movement patterns
• AI transcription
• AI-based eye-tracking
• Phonetic text-to-speech to control the lip sync

Hello there! I am Anushka, a passionate UX designer based in India, with a keen interest in AI-related topics.

Here I am after finally deciding to write articles instead of storing away what I am learning in a dusty Notion document. Hope you had a good read, cheers! :)