How artificial intelligence can change usability testing

UX and Artificial Intelligence it's now ready to help designers, product owners and companies improve the way they gather information from their users.

As the internet continues to grow as the preferred space to communicate with clients, users, families, and communities, the effort designer and product team gather into creating meaningful experiences is requiring new ways to gather data from users. Artificial Intelligence comes in handy in enhancing the capacities to perform usability testing.

Artificial intelligence is becoming more and more common in conversations in the tech industry. One of the questions that I ask myself as a designer is How A.I. will it impact my work in 10 years? As designers our job is mainly directed to design experiences based on Artificial Intelligence (chatbots, Assistants, Marketing ads, etc), but what if we could use Artificial Intelligence and more specifically Machine Learning to design better user experiences?

Our first question here at Odaptos was Is it possible to automate design? With that in mind, we jumped into the long and important task of documenting ourselves as what artificial intelligence could offer.

We ended up with a huge list of possibilities, some quite crazy but some others extremely doable and we were amazed at the ocean of possibilities that were before us, the next step was to see what parts of the design process could be affected and how we could improve our own process as experience designers.

Our strong connection to human-centered design drove us to focus our attention on the Empathy Mapping methodology, created by Dave Gray, a method that has gained a lot of traction in the agile community and it’s now a common practice in product design.

What is Empathy Mapping?

An empathy map is a collaborative visualization used to articulate what we know about a particular type of user. It externalizes knowledge about users based on what they say, do, think, and feel, in order to 1). Create a shared understanding of user needs, and 2). aid in decision-making.

The process is based on the observation of the user and the attentive notation of the user’s expressions both verbally and non-verbal, the role of the team conducting empathy mapping interviews is to facilitate the manifestation of emotions from the user during the interviews.

The post-it nightmare

If you are a user experience designer like us chances are that after an empathy mapping session you have been looking at a wall that has a considerable amount of post-its on it asking yourself the question Where to begin? This was our problem to solve.

Normally after some usability interviews with users, our teams end up with tons of notes in the form of spreadsheets, notebooks, or recordings, then you spend a considerable amount of time building those notes into comprehensive resumes and one way of making this happen is to stick the most important elements to a surface to give visibility to the whole team.

Artificial Intelligence came to the rescue!

User experience and Artificial Intelligence share a common objective: predict human behavior and anticipate what will happen next. Language and emotional analysis is a common denominator in both disciplines, it’s on that touching point where we saw an opportunity to create something new.

The constant need for teams to gather emotions from the users and the long and tedious process of analyzing results after an empathy mapping interview with several users quickly became in our minds a perfect mixture of need/problem.

Here are 4 Ways Artificial Intelligence can change the user experience interview.

Deep Learning. The way we can get the most of what the user said during an interview and how we can give value to their expressions.

Gestural Data. How we will be able to determine through user actions the non-verbal mind-state of the user.

Facial Recognition. The depth of insight we will be able to have when interpreting their emotions and expressions.

**Semantical Data. **Often users give insight in the form of linguistic keys, meaning they express themselves into code phrases that later are analyzed by UX designers to provide meaning. Using Machine learning we can create a deep understanding of the user intentions and give visibility to hidden information expressed by the user.

The combination of these technologies will allow the platform to extract metadata such as concepts, entities, keywords, categories, feelings, emotions, relationships, and semantic roles by understanding what the user is expressing during the interviews.

Creating a wide relationship between different categories of data aimed to express user emotions on a more dynamic level, offering designers and product owners real insight from human emotions.

Conclusion

Artificial Intelligence can speed up the process of emotional design on usability testing, the constant need to gather information from what the user is expressing without the need to hear over and over again multiple interviews is a must in design thinking. Also, the capacity to overcome the classification of data and the collaborative capacities offered by digital environments make the perfect combination of methodologies.