{
  "abstract": "ObjectiveTo investigate the current status of artificial intelligence (AI) readiness among nursing students and analyze its influencing factors, providing evidence for the cultivation of AI literacy in nursing education.MethodsA total of 684 nursing students from two universities in Hubei Province were recruited as study participants. Data were collected using a general demographic questionnaire and the Chinese version of the Medical Artificial Intelligence Readiness Scale for Medical Students (C-MAIRS-MS). Descriptive statistics, univariate analysis, and multiple linear regression analysis were employed for data processing.ResultsThe overall mean score of AI readiness among nursing students was 3.47 ± 0.31; the cognitive dimension scored the highest (3.57 ± 0.44), while the competency dimension scored the lowest (3.32 ± 0.48). Multiple linear regression analysis revealed that education level, Self-rated academic performance, Student cadre, Innovation and entrepreneurship project, Large language model use frequency, AI course learning, Programming foundation were significant predictors of AI readiness, collectively explaining 33.3% of the total variance (R2 = 0.333, adjusted R2 = 0.311, F = 15.030, P < 0.001)ConclusionNursing students’ AI readiness remains suboptimal with room for improvement. Targeted interventions addressing key predictors are essential to strengthen AI competency among nursing students.",
  "abstract_shown": true,
  "affiliations": [
    "epartment of medicine, Jingchu University of Technology, Jingmen, China"
  ],
  "article_type": null,
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  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hui Qin"
    },
    {
      "affs": [
        0
      ],
      "name": "Tao Zeng"
    },
    {
      "affs": [
        0
      ],
      "name": "Lu Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e1scj36g8qnsf.bib",
  "cited_by": 0,
  "doi": "10.3389/fmed.2026.2007016",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fmed.2026.2007016/full",
  "id": "e1scj36g8qnsf",
  "issns": [
    "2296-858X"
  ],
  "landing_url": "https://doi.org/10.3389/fmed.2026.2007016",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "2718|Artificial Intelligence in Healthcare and Education"
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  "pdf_url": "https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.2007016/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/e1scj36g8qnsf.ris",
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    {
      "first_seen": "2026-10-30T13:47:18Z",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
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  "title": "Status and influencing factors of artificial intelligence readiness among nursing students",
  "title_text": "Status and influencing factors of artificial intelligence readiness among nursing students",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:18Z",
  "url": "https://research.draconitic.com/paper/e1scj36g8qnsf",
  "venue": "Frontiers in Medicine",
  "versions": []
}