Across the research industry, concerns about data quality have become harder to ignore. Low-effort participation, automated responses, and new types of fraud are no longer rare exceptions. They are now part of everyday fieldwork reality.
Glaut’s AI-Moderated Interviews (AIMIs) were created in this context. The methodology integrates quality controls directly into the data collection process, rather than depending on post-hoc cleaning. This approach was recognized with the ESOMAR Award for Breakthrough Research Methodology, not as a claim of final answers, but as a tangible step toward more responsible, transparent data collection.
Traditional web surveys face issues with speeders, bots, and copy-pasted responses.
AIMIs address this by combining the depth of qualitative interviews with the scale and control of surveys, while incorporating automated quality checks that filter out noise before it enters the dataset.
AIMIs combine a predefined interview structure with real-time interaction. Instead of relying on static questionnaires, the system conducts interviews as guided conversations, enabling quality signals to emerge during data collection rather than solely during analysis.
Each interview follows a semi-structured design: researchers set the main questions and goals beforehand, while the AI moderator dynamically creates follow-up questions based on each respondent’s answers.
This ensures that every interview stays methodologically consistent, while the probing is personalized for each respondent, allowing the conversation to delve deeper when relevant and move forward when it is not.
Researchers retain complete control over the moderator’s behavior. They can clearly instruct the AI on tone, formality, pacing, and interaction style to ensure it matches the research context and target audience.
Alongside the moderator, dedicated quality and fraud-prevention agents are present during the interview.
Together, these components create a real-time quality layer. The aim is to make quality risks visible, traceable, and controllable during collection.
Instead of presenting AIMIs as a finished solution, Glaut has invested in a Research on Research program. The goal is simple: to allow independent researchers to test, stress, and challenge AIMIs against established methods in real research settings.
So far, this effort includes four independent studies:
These studies vary in design, population, and metrics. Some highlight strengths, while others reveal limitations. Together, they build a growing empirical foundation for understanding where AIMIs succeed and where caution is needed.
All studies are fully published on the Glaut Research page to facilitate replication, critique, and informed discussion.
Recognition from ESOMAR does not mark the end of the discussion; instead, it indicates a shift in focus towards where methodological attention should be directed.
AIMIs are not meant to replace human interviews. They turn online, static surveys into conversational experiences, adding dynamic probing and adaptive interaction while keeping the scalability of traditional questionnaires.
Their contribution emphasizes improving the depth and clarity of responses by shifting efforts earlier: decreasing reliance on post-fieldwork cleaning and interpretation, and focusing more on data quality during collection.
Check out the full list of the 2025 ESOMAR Award Finalists
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