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Research on Research

As researchers, you don't need hype. You need evidence.

We collaborate with independent researchers and academic institutions to evaluate AI-moderated interviews under real research conditions. Glaut provides full platform access, while our partners run the tests and conduct the analysis independently.

Independent institutions and researchers leading the program

Publications
Check out our research paper on experimental studies
Whithin AIMI evaluation
Talking to a Bot: Panelists, Pure Qual, and Researchers interviewed by AIMI
Lauren McCluskey
Abstract
An independent study by Lauren McCluskey of Responsive Research, Inc. examined whether the source of the sample influences data quality in AI-moderated interviews. It compared 101 panel participants, 28 traditionally recruited qualitative respondents, and 37 qualitative researchers using the same AIMI on menopause. The main finding was that panel and qualitative respondents agreed on concept rankings, and both groups showed high disclosure levels, even on sensitive health topics, without human moderation. Qualitative researchers considered AIMI reliable for validation tasks like concept and message testing, but noted that it lacks the probing depth and emotional nuance typical of human moderators in exploratory research.
Apr 2026
Glaut Inc
Methodology
A comparative study of AI moderation vs. Human moderation using biometrics by Curtin University
Dr. Billy Sung
Abstract
Using biometric data and controlled interviews, Prof. Billy Sung from Curtin University compares AI and human interviewers. The study shows that although humans create stronger emotional engagement, AI interviewers elicit comparable levels of trust and disclosure without increasing participant discomfort. The results offer practical guidance for when AI-moderated interviews can be used at scale.
Feb 2026
Glaut Inc
Methodology
AIMIs (voice + text) vs. Static Online Surveys: Human Highway’s Comparative Study
Amina Aini
Abstract
An independent study by Human Highway evaluated the impact of conversational interfaces on data quality, finding that the AIMI modality significantly outperformed traditional questionnaires across all informational metrics. The research quantified a 30% increase in response verbosity, a 24% rise in conceptual density, and a 58.7% improvement in semantic cohesion. The study further highlights the "voice effect," which generated a 132% increase in word count compared to traditional text entries. Beyond data depth, AIMI enhanced the respondent experience: 92% of participants found the interface easy to use, and 86% felt "listened to or understood."
Jan 2026
Glaut Inc
Methodology
AIMIs vs. Static Online Surveys: A Comparative Study by the University of Mannheim
Aylin Idrizovski
Abstract
A comparative study with the University of Mannheim found that AI-moderated interviews (AIMI) generated higher-quality open-ended responses than a static online survey using the same questionnaire. The responses were more linguistically rich, with increases of 39% in word count, 51% in unique words, and 12% in lexical diversity, without reducing readability or the proportion of content words. AIMI also covered a wider range of themes (+36% unique themes), eliminated gibberish answers (0% vs. 10%), and enhanced the participant experience by 6%.
Dec 2025
Glaut Inc
Methodology
Voice vs. Text in AI-Moderated Interviews: A Comparative Study of Data Quality, Disclosure, and Participant Experience
Veronica Valli
Abstract
When people speak instead of type, they share more, and differently. Across 252 AI-moderated interviews, voice responses were 236% longer, 138% more varied, and 28% richer in themes than text. Yet participants rated all formats equally high for ease, empathy, and openness, with 55% still preferring text for privacy and control. Voice brings depth, text offers comfort, and hybrid AIMIs balance both.
Oct 2025
Glaut Inc
Methodology
A comparative study of AI moderation vs. Human moderation using biometrics by Curtin University
Dr. Billy Sung
Abstract
Using biometric data and controlled interviews, Prof. Billy Sung from Curtin University compares AI and human interviewers. The study shows that although humans create stronger emotional engagement, AI interviewers elicit comparable levels of trust and disclosure without increasing participant discomfort. The results offer practical guidance for when AI-moderated interviews can be used at scale.
Feb 2026
Glaut Inc
Methodology
AIMIs (voice + text) vs. Static Online Surveys: Human Highway’s Comparative Study
Amina Aini
Abstract
An independent study by Human Highway evaluated the impact of conversational interfaces on data quality, finding that the AIMI modality significantly outperformed traditional questionnaires across all informational metrics. The research quantified a 30% increase in response verbosity, a 24% rise in conceptual density, and a 58.7% improvement in semantic cohesion. The study further highlights the "voice effect," which generated a 132% increase in word count compared to traditional text entries. Beyond data depth, AIMI enhanced the respondent experience: 92% of participants found the interface easy to use, and 86% felt "listened to or understood."
Jan 2026
Glaut Inc
Methodology
AIMIs vs. Static Online Surveys: A Comparative Study by the University of Mannheim
Aylin Idrizovski
Abstract
A comparative study with the University of Mannheim found that AI-moderated interviews (AIMI) generated higher-quality open-ended responses than a static online survey using the same questionnaire. The responses were more linguistically rich, with increases of 39% in word count, 51% in unique words, and 12% in lexical diversity, without reducing readability or the proportion of content words. AIMI also covered a wider range of themes (+36% unique themes), eliminated gibberish answers (0% vs. 10%), and enhanced the participant experience by 6%.
Dec 2025
Glaut Inc
Methodology
Voice vs. Text in AI-Moderated Interviews: A Comparative Study of Data Quality, Disclosure, and Participant Experience
Veronica Valli
Abstract
When people speak instead of type, they share more, and differently. Across 252 AI-moderated interviews, voice responses were 236% longer, 138% more varied, and 28% richer in themes than text. Yet participants rated all formats equally high for ease, empathy, and openness, with 55% still preferring text for privacy and control. Voice brings depth, text offers comfort, and hybrid AIMIs balance both.
Oct 2025
Glaut Inc
Whithin AIMI evaluation
Talking to a Bot: Panelists, Pure Qual, and Researchers interviewed by AIMI
Lauren McCluskey
Abstract
An independent study by Lauren McCluskey of Responsive Research, Inc. examined whether the source of the sample influences data quality in AI-moderated interviews. It compared 101 panel participants, 28 traditionally recruited qualitative respondents, and 37 qualitative researchers using the same AIMI on menopause. The main finding was that panel and qualitative respondents agreed on concept rankings, and both groups showed high disclosure levels, even on sensitive health topics, without human moderation. Qualitative researchers considered AIMI reliable for validation tasks like concept and message testing, but noted that it lacks the probing depth and emotional nuance typical of human moderators in exploratory research.
Apr 2026
Glaut Inc
Our Principles
01. Evidence over Assumptions

We test before we tell.

Every claim about AIMIs is grounded in comparative experiments, evaluating performance, engagement, and data quality against established methods like surveys, IDIs, and CATI.

A full-service agency crafting digital experiences that inspire awe and wonder.

02. Experimentation as Method

We design studies to learn, not to confirm.

From dynamic follow-ups to voice-based interviews, each experiment challenges how research is conducted and opens new possibilities for scalability, empathy, and precision.

03. Inclusion by design

Research should reach everyone.

We explore how AI-moderated interviews make participation accessible to children, elderly adults, and other groups often excluded by traditional methods.

04. Collaboration as Validation

We believe rigorous research is collective work.

Every study is run internally at Glaut and in partnership with independent researchers, MR firms, and academic teams to ensure transparency, credibility, and shared advancement.

Live Events
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We are hosting a webinar series to examine the performance of AIMIs from a methodological perspective. Click the event you’re interested in and register for free.

Call for Researchers

Together, we advance research.
Join us in testing and shaping a new hybrid methodology

Join Us
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Researchers have already partnered with us across Europe, Australia and the US.

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Comparative studies in progress, exploring data quality, empathy, inclusion, and efficiency.

Collaborate with Glaut to test, compare, and evolve AI-Moderated Interview methods through open, evidence-based experimentation.