在书面推信中实施人工智能
Robert Snedegar1, Courtney Pilkerton1, Jun Xiang1
1Department of Family Medicine, West Virginia University School of Medicine, Morgantown, USA.
Cureus
|November 17, 2025
概括
人工智能 (AI) 生成的推信 (LoRs) 的评分比传统的LoRs高. 对于质量较低的候选人来说,AI LoRs特别有利,这表明在居住申请中提高标准化的潜力.
科学领域:
- 医学教育 医学教育
- 人工智能在医学中的应用
- 住院招生 住院招生
背景情况:
- 推信 (LoRs) 对于居住申请至关重要,但受到主观性和可靠性方面的担忧.
- 人工智能 (AI) 为标准化LoR质量提供了一个潜在的解决方案.
- 这项研究评估了人工智能产生的与传统的LoRs的比较质量.
研究的目的:
- 为了比较人工智能生成的推信 (LoRs) 与传统的人写的LoRs的质量.
- 评估人工智能产生的LoRs对居住候选人的评估的影响.
- 确定AI是否可以提高LoRs的标准化和可靠性.
主要方法:
- 教师评审人员评估了匿名的LoRs,用于居住候选人,不知道AI的产生.
- 候选人的质量通过采访前的thalamus分数进行了标准化.
- 独立的样本t测试和肯德尔的W测试用于统计分析.
主要成果:
- 人工智能生成的LoR获得的评分明显高于传统的LoR (4.14对3.29,p < 0.0001).
- 对于质量较低的 (4.17对2.85,p < 0.0001) 和质量较高的候选人 (4.13对3.63,p = 0.006) 来说,人工智能LoR的表现优于传统的LoR.
- 对于所有评估的LoRs (肯德尔的W,p <0.05) 观察到高的interrater协议.
结论:
- 人工智能生成的LoR被评为优于传统的LoR,特别是对于质量较低的候选人.
- 人工智能作为一种提高在居住申请中推信的质量和标准化的工具具有前景.
- 对AI在LoR生成中的伦理影响和更广泛应用的进一步调查是有必要的.
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