User Research Synthesis for Designers
Turn interviews and test notes into evidence backed insights, with AI as a careful helper
A taste of a lesson
The AI summary says 'users find onboarding confusing' with a great quote, but I cannot find that quote in any transcript.
Treat it as invented until proven otherwise. Models sometimes produce plausible quotes or merge several remarks into one. Search the transcripts for key words from it; if nothing matches, remove it. Then rebuild the theme from verified notes: which participants actually struggled with onboarding, and at what step? A stronger insight reads like 'Four of six participants paused at the bank linking step because they did not know why it was needed: P1, P3, P4, P6.' Which participants do you remember struggling there?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Prepare and anonymise research data responsibly
- Code observations and build affinity clusters
- Write insights that state a finding, its importance and its evidence
- Use AI for first pass analysis and verify every quote and claim
Lesson plan
- 1 Preparing the data Get accurate, anonymised data ready for analysis. Start
- 2 Observations and coding Break data into atomic notes and tag them consistently. Start
- 3 Affinity mapping Group observations bottom up into meaningful clusters. Start
- 4 From themes to insights Write insights that link findings to evidence and implications. Start
- 5 Verification Check AI assisted outputs before anyone relies on them. Start
- 6 Presenting for decisions Share findings in a way that leads to action. Start
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About this tutor
For designers, researchers and product managers who have run interviews or usability tests and now face a pile of transcripts and notes. This tutor teaches a rigorous synthesis process: preparing and anonymising data, coding observations, affinity mapping, forming themes, writing insights that link to evidence, and presenting findings that lead to decisions. AI can speed up transcription, first pass coding and clustering, but it can also invent quotes, flatten minority experiences and overstate patterns. You will learn checks that keep every insight traceable to what participants actually said and did.
Reviews
4.5
2 ratingsSample
- Beatriz F.Sample
Caught two invented quotes in our AI summaries after this. Very practical verification habits.
- Hiroshi M.Sample
The finding, why, evidence format made our insights much harder to argue with. Stakeholders stopped dismissing them as opinions.
About the teacher
AI in product design: UX workflows, mockups, design systems, slides and motion
9 tutors 331 lessons taught Sample
I teach designers and design curious people how to bring AI into interface and communication design without giving up judgment. I work in UX and interface design, mostly on web and mobile products, and I have watched AI speed up some steps and quietly damage others. My lessons follow a real workflow: research, structure, screens, systems and the way work...
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