人类还是机器? 在个人陈述中对人工智能生成的书面检测进行比较分析
Margaret A Goodman1,2,3,4, Anthony M Lee1,2,3,4, Zachary Schreck1,2,3,4
1Margaret A. Goodman, Program in Physical Therapy in the Mayo Clinic School of Health Sciences at the Mayo Clinic College of Medicine and Science and in the Department of Physical Medicine and Rehabilitation at the Mayo Clinic.
Journal, physical therapy education
|January 14, 2025
概括
人类读者,反复量化分析 (RQA) 和GPTZero准确地区分人工智能生成的人类书面个人陈述. 这些方法为在招生过程中保持学术诚信提供了有价值的工具.
科学领域:
- 医学教育 医学教育
- 健康 专业 教育 卫生 专业 教育
- 学术诚信 学术诚信
背景情况:
- 先进的人工智能的兴起,包括ChatGPT和Google Gemini,对学术提交的真实性提出了挑战.
- 以前的研究表明,在检测人工智能生成的文本方面,成功率可变,需要进一步调查.
研究的目的:
- 为了评估人类读者的有效性,Recurrence Quantification Analysis (RQA) 和GPTZero AI检测工具在区分人工智能产生的与人类写的个人陈述.
- 评估这些检测方法对物理治疗师教育计划录取的影响.
主要方法:
- 分析了50份人类撰写的个人陈述和50份人工智能生成的个人陈述.
- 分析是由两个人类评分员进行的,RQA用于词汇复杂性,GPTZero用于AI特定的文本特征.
主要成果:
- 人类评分器获得了高的一致性 (κ = 0.92) 和准确性 (97%-99%).
- RQA参数 (重复性,最大线) 和GPTZero参数 (简单性,困难性,可读性) 有效地区分了人类和人工智能生成的文本,ROC曲线下的面积> 0.768.8.
结论:
- 人类读者,RQA和GPTZero在区分人工智能产生的个人陈述和人类撰写的个人陈述方面表现出不同但显著的准确性.
- 研究结果强调了这些方法对学术录取的重要性,并建议未来研究综合方法.
- 为申请人,管理机构和机构提出战略,以维护招生过程中的诚信.
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