{
  "abstract": "Surveys can reach large samples but capture only pre-defined answers, with identical responses often concealing very different reasoning. We tested whether AI-moderated interviews capture this reasoning at scale. In a pre-registered, randomized, counterbalanced design, 1,953 US adults from a probability-based panel and an opt-in panel completed three AI-moderated interviews as well as a section with closed-ended and open-text survey items. Topics were extracted from transcripts inductively, without a predefined codebook, and validated by a confirmatory follow-up. Participants endorsed 94% of topics as fair descriptions of what they said. 21-51% of topics from the AI interview had no counterpart in the closed-ended battery, and 17-37 percentage points net coverage over open text answers. Among respondents who gave identical closed-ended responses, their reasoning diverged widely in valence and content. AI-moderated interviews surface reasons that closed-ended and open-text items miss, and show how reasoning differs among people who answer alike.",
  "abstract_shown": true,
  "affiliations": [
    "Surgo Health"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "A. Anwyl-Irvine"
    },
    {
      "name": "M. Economides"
    },
    {
      "name": "T. Engelthaler"
    },
    {
      "name": "S. Sutermaster"
    },
    {
      "name": "H. Kemp"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Sgaier"
    },
    {
      "name": "P. Smittenaar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c0ymj4kane2s0.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.01.26367029",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.11.01.26367029v1.full",
  "id": "c0ymj4kane2s0",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.11.01.26367029v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.11.01.26367029v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c0ymj4kane2s0.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:20Z",
      "native_id": "10.64898/2026.11.01.26367029",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.11.01.26367029v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Same answer, different reasons: AI-moderated interviews uncover topics and reasoning that surveys miss.",
  "title_text": "Same answer, different reasons: AI-moderated interviews uncover topics and reasoning that surveys miss.",
  "type": "preprint",
  "updated_at": "2026-11-05T00:20:36Z",
  "url": "https://research.draconitic.com/paper/c0ymj4kane2s0",
  "venue": "medRxiv",
  "versions": []
}