Moderate structured feedback at scale
Use AI moderation only for a fixed, low-risk feedback guide when consistent administration matters more than exploratory discovery.
Why
The workflow problem
A fixed, low-risk guide must reach more participants than human moderators can cover, without converting structured feedback into unsupported discovery claims.
AI moderation may collect standardised feedback faster where the team already knows what to ask. It cannot make the contextual judgments needed for exploratory, high-stakes, sensitive, or heavily adaptive research.
Concrete output
What you will produce
Structured interview corpus with approved guide, probe rules, completion data, exception log, and human follow-up list.
How
Run the workflow
Qualify the study
The research lead confirms that the objective is structured feedback in a well-understood, low-risk problem space. Exclude discovery, safeguarding-sensitive, crisis, or specialist-domain topics that need adaptive human judgment.
Set the guide and escalation rules
The researcher writes a fixed guide, permitted clarification probes, time limit, participant disclosure, consent language, and escalation triggers. A privacy and safeguarding owner approves the data boundary before launch.
Configure bounded moderation
The researcher configures the AI interviewer to ask only the approved questions and clarification probes, with no unsupported improvisation. Test it against scripted edge cases and confirm that an escalation stops the session.
Configuration instruction
Ask only the approved guide in the stated order. You may use the listed clarification probes only when the answer is incomplete or unclear. Do not introduce new topics, give advice, infer meaning, or claim understanding. If an escalation trigger appears, stop the interview and display the approved handoff message.Audit and follow up
The researcher samples recordings and transcripts during collection, inspects repetitive or failed probes, and assigns exceptions to a human follow-up path. Analyse the corpus as participant data, with the platform behaviour logged separately.
Human–AI partnership
Who contributes what
AI contribution
Administers a fixed guide and bounded clarification probes consistently; it does not interpret nuance or conduct discovery.
Human responsibility
The research lead qualifies the use case, approves guide and consent, monitors sessions, follows up exceptions, and interprets the evidence.
Stop rule
Stop the AI-moderated study on distress or safeguarding signals, repeated probe failure, consent failure, a need for adaptive follow-up, or any move into exploratory or high-stakes discussion.
Check before use
Watch out
A transcript can look complete while omitting the unexpected thread a skilled moderator would pursue. Review recordings as well as summaries, monitor participant experience, and do not generalise beyond the structured questions asked.
Evidence base
What supports this workflow
NN/g’s comparative test of two tools with 10 researchers supports this narrow fit for structured interviews and documents important limitations. The evidence is recent and limited in scale; it does not establish equivalence to human-led discovery interviews.
- Nielsen Norman Group · AI-Moderated Interviews: If, When, and How to Use Them
Provides the directly relevant comparative evaluation and limits AI moderation to structured, well-understood feedback contexts.
- Government Digital Service · Plan a round of user research
Informs session planning, participant consent, recording, and practice-session checks that remain human accountabilities.
- Government Digital Service · User research in discovery
Clarifies why discovery requires understanding current behaviour and context rather than only standardised responses.