检测人工智能生成与人类撰写的医学学生论文:半随机控制研究
Berin Doru1, Christoph Maier1, Johanna Sophie Busse1
1University Hospital of Paediatrics and Adolescent Medicine, St. Josef-Hospital, Ruhr University Bochum, Bochum, Germany.
JMIR medical education
|March 7, 2025
概括
专家可以通过识别语言模式,如冗余和重复来识别人工智能生成的科学文本,而不是内容熟悉. 这项研究突出了区分人类和人工智能写作的关键特征.
科学领域:
- 人工智能 (AI) 在学术界
- 科学写作真实性 科学写作真实性
- 医疗教育 技术 技术 医学教育
背景情况:
- 像ChatGPT这样的AI模型的复杂性挑战了人类与AI文本差异化.
- 由于人工智能文本生成,学术完整性的担忧正在上升,特别是在医学领域.
- 准确的作者归因对于学术工作至关重要.
研究的目的:
- 评估医疗专业人员和人文学科学者区分人工智能生成和人类撰写的德国医学文本的能力.
- 分析专家识别背后的推理,重点关注内容熟悉性和语言线索.
- 评估特定语言特征对作者身份识别的影响.
主要方法:
- 一项半随机的受控研究,涉及35名专家 (22名医学专家,13名人文专家) 评估配对的医学文本.
- 专家们确定了人工智能生成的文本 (ChatGPT 3.5) 和学生写的文本,并提供了理由.
- 对理由的定性分析和对文本特征和专家评级的定量分析.
主要成果:
- 专家们在70%的案例中准确地识别了人工智能生成的文本,医疗和人文群体之间的差异很小.
- 识别准确度在很大程度上独立于内容熟悉度.
- 风格特征,包括冗余性 (OR 6.90),重复性 (OR 8.05) 和连贯性 (OR 6.62),对于AI文本检测至关重要.
结论:
- 医学和人文专家可以根据语言属性识别人工智能生成的文本,而不是内容专业知识.
- 冗余性,重复性和连贯性是科学写作中AI作者身份的关键指标.
- 进一步的研究应该探索基于这些语言特征的培训方法,以改善AI文本检测.
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