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用生成性人工智能提取数据:使用从系统审查数据库中提取的人类数据来评估准确性的决定因素

Thorben Jansen¹, Lucas W Liebenow¹, Ute Mertens¹

¹Leibniz Institute for Science and Mathematics Education, Kiel University.

Psychological bulletin

|December 15, 2025

概括

生成型人工智能 (genAI) 在系统性审查中显示出加速心理数据提取的潜力,但准确性因变量而异,需要人类的仔细监督.

科学领域:

心理学科学心理学科学
研究中的人工智能.

背景情况:

心理学的系统文献评论依赖于准确的数据提取以获得可靠性.
目前的数据提取方法耗时,阻碍了研究效率.
生成型人工智能 (genAI),特别是大型语言模型 (LLM),在医学数据提取方面表现有前途.

研究的目的:

系统地评估genAI的准确性和错误模式,用于在各种心理领域提取数据.
在心理系统性审查中,将genAI提取的数据与人类提取的数据进行比较.
在心理学研究中确定影响genAI提取精度的因素.

主要方法:

将genAI提取的数据与22个系统审查数据库中的2,179项研究的人类提取数据进行了比较.
用八种不同的大型语言模型 (LLM) 来进行数据提取.
分析了 186 个变量的提取精度和错误模式.

主要成果:

对于20%的变量,LLM提取精度是不可接受的,对于15%的变量,准确度是可以接受的,但不高.
准确性最多取决于变量,其次是系统性审查,最少取决于LLM.
与效果大小变量相比,在上下文/主管变量中观察到更高的准确性.
提取精度与变量描述细节和LLM大小正相关.

结论:

在心理学系统性审查中,GenAI显示了加速数据提取的潜力,但目前的准确性是可变的.
准确性受到变量的类型,描述中的细节和LLM功能的影响.
需要进一步的研究,以优化基因AI的使用,同时保持可靠的心理研究的人类控制.

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