[Demo] Stress-testing GenAI in user research synthesis
Summary
In this high-pressure scenario, the challenge was to conduct 17 user interviews in three days and synthesize a comprehensive report in just one additional day. I’ll explore how we used AI to streamline the research process, from transcription to synthesis, and how tools like ChatGPT contributed to efficient data processing and insight generation. We’ll reflect on the potential and pitfalls of using AI in accelerated user research, from practical aspects to more philosophical considerations on potential changes to the research process. Takeaways Practical insights into integrating AI with traditional research methodologies to expedite the research process An overview of the effectiveness of AI transcription and synthesis tools in real-world research scenarios Critical examination of AI's role in data processing and how it compares with human analysis Strategic considerations for service designers when employing AI to support rapid user research Reflection on the ethical implications and potential impact on the quality of insights and researcher well-being when relying on AI to speed up research processes
Key Insights
-
•
Generative AI speeds textual content generation and navigation but needs human guidance for meaningful interpretation.
-
•
AI struggles to track and assign specific interviewees to user archetypes without explicit human instruction.
-
•
A semi-automated workflow combining AI brainstorming and human refinement delivers faster synthesis under tight deadlines.
-
•
ChatGPT excels at brainstorming clustering parameters and generating detailed archetype descriptions when properly guided.
-
•
AI can retrieve relevant interview quotes but often requires multiple prompt iterations to produce longer, contextually fitting excerpts.
-
•
Visual generation of archetypes using DALL·E currently yields poor results and often contradicts instructions.
-
•
Token limits can constrain transcript processing, but newer models like GPT-4 improve handling of larger inputs.
-
•
AI behaves like an 'alien intern'—powerful but unaware of design research processes and simple context details unless taught.
-
•
Using AI as a creative partner helps overcome cognitive overload, especially in rapid, solo research scenarios.
-
•
Less experienced researchers require caution when using AI tools, as expert evaluation is crucial for output quality.
Notable Quotes
"Gene AI can really help to speed up synthesis moments connected to textual content generation and navigation."
"AI behaves like an alien intern because it’s not capable of making sense of some things easy for us."
"I approached the GPT as if it were my human colleague, asking very open questions."
"It couldn’t find specific interviewees for user archetypes, so I had to adapt and guide the process."
"This is a hero moment because it accelerates and simplifies exploration of clustering options."
"Text generation is the biggest superpower of ChatGPT in research synthesis right now."
"Visuals from DALL·E didn’t work well; I asked multiple times to remove cables but they still appeared."
"AI can help manage cognitive overload and speed up work when you have short timeframes and no team."
"You need a lot of annoying handholding with the AI: reminding it of how many people you have or simple things."
"I wouldn’t leave AI tools to juniors because it’s crucial to have your own experience to evaluate outputs."
Or choose a question:
More Videos
"Embracing uncertainty means diving in without knowing the outcome and creating something new every time."
Jim KalbachJazz Improvisation as a Model for Team Collaboration
November 6, 2017
"Inclusion, collaboration, and iteration are the three pillars of how we work with authors to make a book."
Louis RosenfeldCoffee with Lou: Should You Write a (UX) Book?
March 7, 2024
"I kind of think of it one as like fields of influence — staff influences the team or pillar, principal influences organization-wide."
Catt Small Micah Bennett Brian Carr Jessica HarlleeWhat's Next for ICs: Exploring Staff and Principal Designer Roles
February 22, 2024
"We can’t be there for every behavior after the nudge, nor do we necessarily want to be."
Marieke McCloskeyUser Science: Product Analytics & User Research
March 11, 2021
"Blurring user faces loses context and doesn’t address voice privacy, making it an inadequate solution."
Llewyn Paine[Demo] Deploying AI doppelgangers to de-identify user research recordings
June 5, 2024
"If you cannot remove a treatment, it may not be a true cause."
Joshua NobleCasual Inference
October 6, 2023
"Not all learnings lead to crucial conversations, but big insights often do, requiring emotional awareness."
Sara LogelYour Colleagues are Your Users Too
March 29, 2023
"Learning is a marathon, not a sprint, especially in the context of online conferences."
Bria Alexander Louis RosenfeldWelcome
January 8, 2024
"Confidence is a higher burden in retail because people are giving real money; inaccessible flows cause quick abandonment."
Sam ProulxOnline Shopping: Designing an Accessible Experience
June 7, 2023
