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This Week's Reads:

🧭 Frontier UX Research Circa May 2026: Holbrook argues that becoming "AI-native" in UX research isn't a tooling switch — it's a redesign of how evidence flows from collection through synthesis to decision. He maps three layers researchers need to move on in parallel: a mindset shift toward continuous discovery, an operating model where AI executes repeatable tasks while humans hold judgment and interpretation, and infrastructure investment in shared repositories and governance so AI agents can query the team's actual evidence base. The piece is most useful for its framing of "context engineering" — feeding AI strategic hypotheses and study plans rather than generic prompts — and for the staged analysis pipeline with explicit human checkpoints between raw transcript, structured transcript, candidate themes, and insight briefs.

🤝 AI in Research: Pace and what holds: A reflective piece from Checkout.com's UX Research team — Jithin, Simon, Alcinda, and Orrin — on what AI has actually changed about their work, and what they're choosing to keep human. They land on a three-zone framing: data in (still the researcher's craft — relationships, body language, trust), synthesis (where AI changes the game, with guardrails built behind a queryable repository), and communication out (still storytelling, still a human in the room). The hard lines are explicit: no synthetic respondents, no AI predicting design outcomes before a human has reviewed, no AI-authored reports going out without a researcher's name on them. The most uncomfortable thread is the team's honesty about output volume, role anxiety, and the question "why are we saving the time?" — if the answer is to do more meaningful work, good; if it's because we can, pause.

🧪 Exploring Agent-Assisted Qualitative Analysis: Shankar runs the empirical test most of the recent AI-in-research takes have been waiting for: six experimental conditions on 451 tweets about why people switch from Claude, varying agent autonomy and where humans intervene (none, mid-process code review, memo feedback, hierarchical multi-agent). Across every condition, agents fail in fundamental ways — they paraphrase instead of analysing, reuse codes poorly (93–100% of codes appear once), only cover 6–68% of the corpus depending on setup, and don't adapt well to evolving human preferences. The most useful finding for working researchers isn't that AI can't code data; it's that interface design matters as much as model capability. Poor visibility into what changed between rounds, and low-leverage review work, created fatigue that outweighed the speed gains.

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