摘要：人工智能正深刻变革社会科学的研究范式，为人力资源管理领域的方法论创新提供了新契机，但我们对人工智能赋能人力资源管理研究还缺乏全面认识。本文采用系统性文献综述法，以79本核心国际期刊为检索来源，基于大语言模型辅助筛选与人工全文评估的两阶段流程，最终获取143篇文献，旨在厘清人工智能在人力资源管理研究方法中的应用路径、分布特征与发展趋势。研究发现，人工智能的应用主要聚焦于三条路径：一是变量识别与测量，利用自然语言处理等技术将文本、音频、视频等非结构化数据转化为可量化的变量，突破传统自陈式问卷的局限；二是验证变量间关系，借助机器学习算法探索传统线性模型难以捕捉的复杂非线性关系；三是主题归纳与定性解释，运用主题建模等技术从大规模文本中提炼核心主题。三条路径的共同特征在于对非结构化数据的量化和分析，这正是人工智能相较于传统研究方法的核心优势。从应用分布看，招聘与配置、员工关系管理是采用人工智能进行研究最集中的两个领域，主要得益于这两个领域积累了大量易于获取的数字化文本与行为数据；而薪酬福利、培训开发等领域采用人工智能进行研究的文献有限。技术路径以机器学习和自然语言处理为主，大语言模型的兴起正逐步拓展人工智能在质性研究中的应用空间。未来研究应提升AI方法论的透明度与报告规范性，建立统一的技术术语体系以促进跨学科交流；应发挥大语言模型在理论生成与质性分析中的潜力，从而做出更大的理论贡献；需审慎应对大语言模型介入研究全流程引发的数据保密、结果可复现性及过度依赖等伦理挑战，确保AI赋能研究创新的同时不损害学术诚信与科学严谨性。 

Abstract: An increasing number of scholars have begun applying artificial intelligence(AI) to human resource management(HRM) research. Drawing on a systematic literature review of 143 studies, this paper aims to map the current state and future trajectories of AI applications in HRM research methodology. The findings reveal that AI-driven methodological approaches primarily concentrate in three areas: variable identification and measurement, testing relationships between variables, and thematic induction and,interpretation. These three applications collectively highlight AIs core advantage over traditional research methods—namely, its capacity to quantify and analyze unstructured data. Existing research is predominantly focused on the functional domains of recruitment and staffing as well as employee relationship management,with machine learning and natural language processing constituting the dominant technical pathways. Future research should prioritize enhancing the transparency and reporting rigor of AI methodologies, deepening theoretical contributions, and thoughtfully navigating the ethical and practical challenges posed by large language models throughout the entire research process.