Question 1

How is this different from using a general-purpose AI assistant to run interviews?

Accepted Answer

A capable AI can hold a fluent conversation and probe at scale. What general-purpose tools lack is the discipline around it: a method fixed before anyone joins, every finding tied to what a respondent actually said, abstention when the evidence is thin, and results that hold up when someone checks them. That discipline, not the fluency, is what makes a result you can act on.

Question 2

How do you know the results are valid enough to base real decisions on?

Accepted Answer

Because the measurement discipline is built in at every step. Constructs are defined in advance with behavioral anchors, scoring is calibrated against human-scored reference sets, the system abstains when evidence is thin, and every score traces to the respondent's own words. Together these meet the expectations the Standards for Educational and Psychological Testing set for defensible measurement (AERA, APA, & NCME, 2014).

Question 3

Do people answer differently because they are talking to an AI, and does that bias the results?

Accepted Answer

Two things work in its favor. Every respondent meets the same instrument, so the interviewer variance that accumulates across a human team disappears. And a non-judgmental interviewer often draws out candor that people withhold face to face. The instrument also flags thin or evasive evidence rather than scoring it, so low-signal conversations do not pass themselves off as findings.

Question 4

How many participants does an interview need, and can it run at survey scale?

Accepted Answer

As many as the question requires. Because one AI interviewer speaks with every respondent in parallel, the same instrument runs from a handful of participants to tens of thousands, with the protocol held invariant so the scores stay comparable. Sample size becomes a decision about the evidence you need, not a limit the method imposes.

Question 5

What if you don't already know what to measure yet?

Accepted Answer

When the qualities that matter can be named in advance, the instrument measures them and reports scores. When the goal is to map a situation before anyone can name those measures, the same instrument gathers and organizes what participants report in their own words. The method changes; the standard of proof does not. Scored or not, every finding must trace to specific things real people said, unsupported claims are held back, and the procedure is documented and repeatable. What makes a finding trustworthy is the method behind it, whether or not it ends in a number.

Question 6

When is a traditional survey or interview still the right tool?

Accepted Answer

Whenever a question needs only two of the three demands. A short pulse check with very well-defined rating scales and questions across a large population is well served by a survey. A deep conversation with a handful of experts is well served by an interview. This approach is built for the harder case, when a question needs depth, scale, and rigor at the same time.

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