人工智能在领导力胜任力发展与选拔中的应用：一项实证研究

Use of artificial intelligence in leadership competency development and selection: An empirical study

——《咨询心理学杂志》第78卷，第3期，2026年3月——

<Consulting Psychology Journal> Volume 78, Issue 1, March 2026

【摘要】在人才选拔流程中，人们采用了多种多样的工具；随着人工智能（AI）技术的不断演进，AI解决方案的开发已逐步渗透至员工选拔的特定领域，尤以申请人简历筛选及非同步视频面试环节为甚。然而，利用结合机器学习的AI模型来解读基于文本的评估中心（AC）模拟测试作答内容，这一领域目前仍鲜有人涉足。本研究的主要目的在于评估某款AI模型的聚合效度与效标效度。本研究的次要目的则是探究：相较于人类评估者（其评分常受限于“范围受限”效应），该AI算法在对书面模拟作答进行评分时，是否能更充分地利用评分量表上的分值区间。研究人员利用AI技术对15,000名领导者的基于文本的AC模拟作答输出进行了分析——这些文本数据总量高达3300万字——并从中识别出了38项胜任力。随后，该AI模型被用于对来自三个独立领导者样本的模拟测试结果进行评分，以此评估其聚合效度与效标效度。研究结果显示，该模型的聚合效度介于0.63至0.73之间，效标效度则介于0.51至0.54之间。在评分范围的利用方面，标准差指标显示，该AI模型所使用的评分分值区间较人类评估者更为宽泛。本研究基于三个独立样本所呈现的实证结果表明：首先，AI算法在对书面文本进行评分时，其评分方式与人类评估者具有高度相似性。其次，AI算法在充分利用可用的全部分值区间方面表现得更为出色。该AI模型在各项指标上似乎已达到与人类评估者比肩的水平——其准确率与召回率指标均高达0.91——这预示着AI模型在未来有望辅助甚至替代人类评估者开展工作。

【关键词】人工智能，领导力胜任力，选拔与评估，领导力发展，人工智能与机器学习

[Abstract] A wide range of instruments is employed within the talent selection pipeline, and, with the progressive evolution of artificial intelligence (AI), the development of AI solutions has made inroads into certain areas of staff selection, notably the processes of reviewing applicant resumes and conducting asynchronous video interviews. However, the use of AI models with machine learning to interpret text-based assessment center (AC) simulation responses has remained largely unexplored. The main aim of this study was to assess the convergent and criterion validity of an AI model. A secondary objective of the study was to see if the AI algorithm utilized more scale points in rating written simulation responses compared to human assessors’, whose scores typically suffer from range restriction. AI was used to analyze the text-based AC simulation outputs of 15,000 leaders, comprising 33 million words, and 38 competencies were discovered. The AI model was then used to score the simulation results of three separate samples of leaders to assess its convergent and criterion validity. The results showed convergent validities ranging from 0.63 to 0.73 and criterion validities ranging from 0.51 to 0.54. In terms of range utilization, the standard deviation indicated that the AI model utilized a wider range of scores than human assessors did. Empirical results presented in this study across three samples suggest that AI algorithms can score written text in a similar way as human raters. Second, the AI algorithms are better at utilizing the full range of scores available. The AI model seems to be on par with the human rater, with accuracy and recall metrics at 0.91, indicating the possibility of augmenting or replacing the human assessor.

[Key words] artificial intelligence, leadership competencies, selection and assessment, leadership development, artificial intelligence machine learning

论文原文：Bronkhorst, P. V., & Becker, J. (2026). Use of artificial intelligence in leadership competency development and selection: An empirical study. Consulting Psychology Journal, Volume 78, Issue 1, Pages 1–26. March 2026. https://doi.org/10.1037/cpb0000288

（翻译兼责任编辑：MARY）

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