The Tool Desk
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What an AI-moderated study can—and cannot—do
An AI-moderated interview uses a discussion guide to conduct a qualitative conversation and ask follow-up questions based on a participant’s answers. It sits between a fixed survey and a human-moderated interview: it can probe open-ended responses and conduct sessions in parallel, but its questions and adaptation are bounded by the guide and configuration.
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Decode describes Mira as facilitating interviews, probing answers, and producing transcripts and synthesis outputs. Treat those outputs as research materials to inspect, not as final findings. A well-written guide can direct the system toward useful examples and clarification; it cannot guarantee that a probe will capture the meaning a human researcher would have pursued.
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Define what you need to learn and what decision the learning will inform. A broad goal such as “understand our users” is difficult to guide or interpret. Narrow it to a behavior, experience, or decision—for example, what people do when they encounter a particular step in a product flow, or why they chose one option over another.
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Keep the first study’s scope small enough that its questions, participant criteria, and intended use of findings are clear. If you cannot explain what kind of evidence would help answer the question, refine it before building the guide.
2. Write a guide that gives follow-ups a purpose
Organize the guide around a short introduction, context and consent language, core questions, and instructions for when to ask for an example or clarification. Pair each main question with the kinds of follow-up that would deepen the answer. This makes the intended depth explicit and reduces the risk of probing that wanders beyond the study.
- Ask about a specific experience or behavior rather than relying only on opinions or hypothetical preferences.
- Use neutral wording so the question does not suggest a preferred answer.
- Identify what an unclear or brief answer should trigger, such as asking for a concrete example or what happened next.
- Keep probes relevant to the research question; extra probing is not automatically better.
The AI can adapt within the instructions it receives. It should not be expected to improvise without limits or to repair a guide whose questions are vague, leading, or unrelated to the decision.
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3. Recruit for relevant experience
Specify the experience or behavior that makes someone able to answer the study question. Screen for that experience rather than relying only on demographic categories. Your criteria should distinguish participants who have encountered the situation from those who have not, and should fit the claim you intend to make from the interviews.
Recruit participants who can speak from direct experience. If the study concerns a specific task or recent event, make that condition clear in the screening criteria and participant-facing information. Do not interpret a set of interviews as representative of a broader population unless the recruitment and study design support that conclusion.
4. Configure and preview the participant experience
Set the session modality, language, expected length, probing depth, and disclosures to suit the question and participant group. Include clear consent and context language, and make sure participants understand that the conversation is AI-moderated and how their responses will be used, as applicable to your study and organization’s requirements.
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Preview the participant-facing flow before invitations go out. Check the instructions, transitions, question wording, and any points where participants may need to provide consent or understand what happens next. The available source material does not establish specific Mira interface labels or configuration paths, so use the current product interface rather than relying on assumed menu names.
5. Complete a test session before launch
Take the study as a participant would. The purpose is to catch confusing wording and friction before they affect recruited sessions, not to treat one test as proof that the study is sound.
- Can a participant understand each question without extra explanation?
- Do follow-up questions invite useful examples or clarification without feeling repetitive or intrusive?
- Does the session flow make sense from introduction and consent through the final question?
- Are there technical or access problems that could prevent a participant from completing the session?
Revise the guide or configuration where the test reveals a problem, then review the participant-facing flow again.
6. Launch carefully and monitor sessions
During launch, look for completion patterns and session behavior that may signal a confusing question or other friction. Apply the platform’s available quality controls and review a sample of transcripts rather than assuming that completed sessions are necessarily usable. If a pattern suggests participants are misunderstanding a question, address the cause before relying on later answers.
Keep monitoring proportional to the study: the goal is to spot issues that compromise the evidence, not to treat automated completion as a quality measure by itself.
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Use transcripts, themes, and summaries as starting points for analysis. Check proposed patterns against representative passages in the underlying sessions, including answers that complicate or contradict a theme. Automated synthesis can misclassify responses or flatten differences in what participants meant; preserve context when deciding what a quote or pattern supports.
Separate what participants said from your interpretation and from the product decision you recommend. The study can inform a decision, but its conclusions should be limited to the participants, questions, and evidence actually covered.
Should the first study use AI, a human moderator, or both?
Choose the moderation approach based on sensitivity, the need for rapport and improvisation, desired scale, turnaround, and the signals the study needs. AI moderation can suit structured questions where consistency and parallel sessions matter. Human moderation retains an advantage when the topic is sensitive, high-stakes, exploratory, or likely to require following an unexpected thread. A hybrid approach can use AI interviews to identify possible patterns, then human sessions to investigate selected issues in greater depth.
Great Question’s guide also describes AI-led interviews with adaptive follow-ups and outputs including transcripts and recordings, while noting the continuing value of human moderators for sensitive, high-stakes, and deeply exploratory work. Those are company-described product details and may change; they are not an independent performance comparison.
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What Mira’s capabilities establish
Decode identifies Mira as its AI Moderator and describes interview facilitation, probing, and generated research outputs. These are vendor descriptions; no independently validated performance figures are established here. Evaluate the study’s evidence by reviewing its sessions and checking whether the guide produced relevant, interpretable answers—not by treating a feature description or generated summary as validation.
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