{
  "abstract": "This study examines students’ questioning as an interaction mechanism in student–LLM philosophical dialogue and how it relates to self-reported critical thinking (CT) and creative thinking (CrT) engagement. Participants were 106 first-year postgraduate students in a Chinese university general education course who completed an end-of-semester assignment using an institutional ChatGPT model embedded in the learning platform. Student questions in dialogue transcripts were coded into six categories aligned with the revised Bloom’s taxonomy. We then applied latent profile analysis (LPA) to identify static questioning profiles and group-based trajectory modeling (GBTM) to capture longitudinal development across conversation rounds, followed by subgroup comparisons on perceived CT and CrT. LPA yielded three profiles: Fact-focused, Explanation-focused, and Evaluation-focused Questioners. GBTM revealed two trajectories with a key divergence around Rounds 5–7: some students plateaued at application and analytical questioning, while others progressed toward evaluative and exploratory questioning. Fact-focused Questioners reported lower CT and CrT engagement scores; plateauing trajectories reported lower CT. Findings highlight how questioning patterns are associated with differential self-reported cognitive engagement in student–LLM dialogue and inform scaffold design to promote higher-order inquiry.",
  "abstract_shown": true,
  "affiliations": [
    "School of Humanities and Social Science, the Chinese University of Hong Kong, Shenzhen, China",
    "Institute of Education, Nanjing University, Nanjing, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jingru Qu"
    },
    {
      "affs": [
        1
      ],
      "name": "Ling Dai"
    },
    {
      "name": "Mengjie Yin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/61c206p0d886p.bib",
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  "doi": "10.1371/journal.pone.0356183",
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    "1932-6203"
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "1710|Artificial Intelligence in Education"
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  "openalex": "W7211935336",
  "pdf_url": null,
  "pmcid": null,
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  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/61c206p0d886p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0356183",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0356183"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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      "origin": "plos:statistics",
      "subfield": "statistics"
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  "title": "What students ask matters: Static and longitudinal questioning patterns in student–LLM dialogue and self-reported cognitive engagement",
  "title_text": "What students ask matters: Static and longitudinal questioning patterns in student–LLM dialogue and self-reported cognitive engagement",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/61c206p0d886p",
  "venue": "PLOS One",
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}