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I remember that a few years ago, UX design for artificial intelligence was just an emerging trend that people liked to talk about.
But today?
It’s reality.
As a UX researcher with over 10 years of experience in product design and software development, I realize when I need to upskill myself in new technology. That’s why I’m telling you that now’s the time to learn about UX for AI if you haven’t already.
We need to accept that AI is here to stay, and sooner or later, every UX designer and researcher will be assigned that involves AI. And you don’t want just to sit there, staring at a blank research plan or Figma page with imposter syndrome. You want to be prepared for the new time; I predict it’s going to happen very soon.
I hope my last statement caught your attention: ‘Every UX designer and researcher will be assigned that involves AI.’ We shouldn't place AI at the centre whenever we research and design an AI solution. We still need to put humans at the forefront. To do that, we need to understand how AI can enhance users and UX by keeping human-centred interactions in the centre while still leveraging AI.
Whether you’re a UX designer or a UX researcher eager to stay ahead of the curve or just curious about AI and its impact on UX, this newsletter issue is for you. I share trustworthy resources that I’ve just recently started studying so that you can upskill yourself in creating AI-powered product experiences.
In the traditional design process, we want to understand users’ needs and expectations. That’s the reason why we do UX research.
When we design an AI-driven product, it’s no different.
But, AI adds a whole new level of complexity: how users perceive and interact with the AI itself. So, it becomes even more critical to understand how users think about AI products. Once we know what users expect from an AI feature, we can design it accordingly to build trust and give them a seamless experience.
Here are 5 foundational pieces you need to focus on when designing for AI.
When designing AI-driven products, it’s important to identify users’ mental models about AI, which means how users perceive and interact with the AI technology. Essentially, we need to understand what users think an AI-driven product or feature does so we can make design choices that align with and support their expectations.
We need to invest as much effort into designing the interface as we do in developing the underlying data and algorithms. In AI user experience, both elements are equally important in creating a successful product. I’d even say UI is even more important here because, as of today (Apr 2024), we don’t have as many design patterns foundationalized as in traditional web design.
Imagine you design a bio-medical application leveraging AI for scientists who work on drug treatments. Your specific users (the scientific users) may look for specific information at specific times and might need various kinds of data. You need to design the interface to accommodate the diverse search behaviours of scientists, presenting them with the right information at the right time without overwhelming them with too much data.
For AI-driven products, it’s important to balance how much users trust the technology. You don’t want users to be confused about where the AI’s results or predictions are coming from, as this could lead to mistrust. But you don’t want them blindly trusting the AI, either. The goal is to find a sweet spot where users understand both the strengths of the AI and the value of their own skills and judgement.
The UX, data, and engineering teams need to collaborate closely. This ensures that the data supports the design of AI-driven features, which are built around the actual needs of users.
If you want to get started in UX Design for AI, here are my top 3 recommendations:
🤖 Google People + AI Guidebook
🤖 Microsoft Guidelines for Human-AI Interaction
You know you can trust them because these companies have been around for decades and put years into researching UX patterns and fundamentals for AI.
In the past couple of weeks, I enjoyed the following resources. Check them out; you might like them, too:
🔗AI users are neither AI nor users by Debbie Levitt
🔗 AI for UX by the NNGroup
🔗 AI as a UX assistant by the NNGroup
🔗 Design for AI with Dan Saffer by vaexperience