Hello,
my name is Tiffany Kirra and I'm a UX researcher at Google on the material
design team.
I will be presenting the case study "exploring the future of design tooling:
The role of artificial intelligence in tools for user experience professionals".
So what value, if any, does AI bring to the design process?
And by AI, (artificial intelligence),
we mean things that are truly driven by AI as well as things that users perceive
to be driven by AI.
A little bit of background on artificial intelligence in design tools in
particular, it's rapidly becoming embedded in design workflows. For example,
Figma has an auto layout resizing feature,
and also the prior work in this area suggests that AI can
help in design tooling,
particularly with automating repetitive tasks and supporting early stage design
exploration.
But what's missing is how is AI perceived by user experience professionals.
So that leads us to our research questions. The first one is,
how do UX professionals perceive AI-driven assists in design tools?
The next one is,
what are their concerns around accepting AI in their day-to-day tools?
And finally,
which design related workflows are promising for the future of AI-driven design?
To get at this,
we surveyed UX professionals to understand their perceptions of AI in design
tooling. Digging deeper into the methods,
we did a online survey of UX professionals at a large
multinational technology company. We conducted the survey in May, 2022,
which is before the release of chat G P T and the other
generative AI tools that are now on the scene in
2023. 71 UX professionals began the survey
out of which 51 completed it for a 71.8% completion rate.
The table here on the right side shows the different roles that
completed the survey as well as their tenure in that role. As you can see,
a lot of designers and researchers took the survey.
For the survey design, we included 18 questions,
nine of which were closed ended, such as Likert scale questions,
and nine of which were open-ended.
We put them into four categories. The first one is demographics,
so we were really trying to understand who it was that was taking the survey.
Next, we asked them about attitudes towards AI,
both in general and within design tooling.
And then we presented them with speculative vignettes for designing with AI.
For this one,
we came up with three vignettes that we thought were reasonable based on prior
work that we've done and showed each person one randomly
selected vignette, so they did not see all three. And then finally,
we asked use of personal data questions. You know,
how comfortable they would be using various forms of data to train AI for
the data. For the open-ended questions,
we conducted a thematic analysis where multiple coders went through the data and
created and discussed the codes that they came up with from which we
compared with the quantitative data. We derived for themes,
AI for creativity support, AI for mundane task support,
use cases for AI in design tools and challenges around AI acceptance.
And we'll dig into each one of these in the findings.
So let's move on to that. The first finding here,
AI for creativity support. UX professionals sought AI as their creative partner.
So this broke out into two topics.
The first one is UX designers. They had certain ideas,
and then UX researchers and content writers came up with different things.
So for UX designers, they're really interested in supporting ideation,
having AI as their creative partner in that way. So for example,
finding and summarizing other designers work.
They want it to show them what's happening,
showing them how other designers have approached design related problems in
the past to help them with problem solving.
Organizing ideas during their brainstorming sessions,
and finally generating variations of UI. Interestingly,
this came up before it was widely available because this survey was taken
before the release of chat GPT.
And then for UX researchers and content writers,
they wanted their creative partner AI to keep them in the flow. So for example,
reviewing and refining their writing,
providing natural language descriptions for visual designs that they're working
with and generating content based on an outline that they provide.
The next finding, AI for mundane task support:
my design intern. So people felt that AI in
this way could focus as an intern for them, helping them out.
Across all UX professionals,
they requested help with tasks that they found repetitive.
For example, the AI could take on things that were currently manual,
but that are straightforward enough for the AI to be able to learn.
The AI could do tasks that currently interfere with the user's ability to focus
on the creative work that they really want to do.
And also it could catch errors that often appear in repetitive work.
Some examples that they gave really focused on language-based tasks.
For example, finding initial themes in the data, synthesizing,
brainstorm data during a design sprint and transcribing and annotating user
interview data. This was especially prevalent amongst UX researchers.
In both of these cases,
users were over and over telling us that they wanted control over the final
outcomes.
So they wanted to be able to accept or reject the suggestions of AI or turn it
off if they felt like they didn't need it.
In the next finding, use cases for AI and design tools,
this goes back to the speculative vignettes that we showed users.
So over here on the right, you can see a table,
a graph with three vignettes. AI to convert a design,
it would convert your design into something.
AI for accessibility,
where the AI would help out with accessibility checking.
AI for new UI would generate new UI.
And as you can see here from the percentage of people who
selected they were somewhat likely or extremely likely to use this type
of tool, most common was vignette two:
AI for accessibility emerged as the clearest use case. So why is that?
Firstly,
they said that accessibility is very important in design. However,
accessibility guidelines are hard to keep up with and understand,
and manually checking for accessibility is inefficient and error prone.
So the UX professionals were hopeful that AI in the future
could suggest accessibility improvements,
correct their errors early in the design process,
and even help them learn more about how to design for accessibility.
There were of course concerns around AI acceptance.
We asked a few questions on this,
and the results came up in two areas.
The first one is workflow related.
The second one is trust and reliability and ethics.
So for workflow related concerns,
UX professionals were concerned whether the AI capabilities have advanced enough
to be useful for design work specifically.
They were also concerned that their workflows have a lot of unique events.
So it might be hard to distill patterns for AI to be trained on.
As far as ethics and trust and reliability, within the ethics
concerns there were concerns about introducing existing biases into
their designs. Basically the AI would be learning off of poor design patterns,
for example. Misuse of AI, widely,
is it going to harm people?
And privacy and use of personal data concerns over using data
that they don't find acceptable. As far as trust and reliability goes,
there were concerns both ways here. Firstly,
quality of the AI input, what the AI is being trained on,
is it good quality? And then also quality of the AI output.
Can I trust what the AI spits out to me to be true and correct and useful for
me? So to recap all of this,
AI-driven design tools should firstly be humble. They should state limitations,
and should be able to understand what's possible with this tool and what's not
so they can calibrate their expectations. Secondly, of course,
be properly trained. This is very important and it seems easy,
but we know that this is actually very challenging. And then finally,
give users the ultimate control to accept or reject suggestions given by AI.
These are the early days for AI and design tooling.
More research is needed to understand this rapidly evolving landscape,
especially as generative AI enters the design tooling market.
So yes, more research will be needed.
Thank you very much for listening. If you have any questions,
please reach out me at tknearem@tgoogle.com. I would love to chat with you.
Thank you again.