Published 10 September 2026 · Source dated 9 September 2026
As AI tools take on transcription, tagging and synthesis grunt work in UX research, the core discipline — clear questions, honest sampling, careful interpretation — hasn't changed. The risk is treating AI summaries as a substitute for actually looking at the data.
Coach
Next time you use an AI tool to summarise research notes, spot-check its output against two raw transcripts before you present the findings.
Surf this topic: UCD methods (opens in a new tab)
Source: nngroup.com (opens in a new tab) ·
Jakob Nielsen's ten usability heuristics have been the backbone of UX critique since 1994. They're worth relearning properly, not just skimming the list.
nngroup.com (opens in a new tab)
Your move
Pick one screen from your own portfolio and run it against all ten heuristics — write down which one it breaks hardest, and fix that first.
AI tools can generate screens fast, but they don't validate usability — that's still on you. Classic heuristic evaluation is more relevant, not less, now that production speed has gone up.
nngroup.com (opens in a new tab)
Your move
before you ship your next AI-assisted screen, run a quick 10-point heuristic check against it and write down what fails.
More first drafts are arriving via AI agents, but the fundamentals of a good design critique haven't moved. IxDF's literature on critique still holds regardless of who — or what — made the first version.
interaction-design.org (opens in a new tab)
Your move
next time you review an AI-assisted design, ask 'what user problem does this solve?' before commenting on how it looks.
