标题： 人工智能增强的研究过程：历史学家的视角 显示英文标题

标题： The AI-Augmented Research Process: A Historian's Perspective

摘要： 本文详细介绍了人工智能，尤其是大型语言模型如何融入历史研究工作流程的案例研究。 该工作流程分为九个步骤，涵盖了从问题提出到传播和可重复性的整个研究周期，并包括两个框架阶段，分别处理设置和文档记录。 每个研究步骤在三个操作领域中进行映射：1. LLM，指分配给语言模型的任务；2. 思维，指历史学家的概念和解释性贡献；3. 计算，指基于传统编程的方法，如Python、R、Cytoscape等。 该研究强调，LLM并不是领域专业知识的替代品，但可以支持并扩展历史学家处理、验证和解释大量文本语料库的能力。 同时，它突出了严格质量控制、交叉核对输出结果以及保持学术标准的必要性。 通过对三位上海商人的深入研究，本文还提出了一种基于真实案例研究的结构化工作流程，该流程将历史学家的认知劳动与计算工具和生成式AI结合起来。 本文通过展示如何通过透明和可重复的工作流程负责任地将人工智能纳入历史研究，做出了方法论和认识论上的贡献。 本文旨在为面对日益复杂的AI增强学术环境的历史学者提供实用指南和批判性反思。 显示英文摘要

摘要： This paper presents a detailed case study of how artificial intelligence, especially large language models, can be integrated into historical research workflows. The workflow is divided into nine steps, covering the full research cycle from question formulation to dissemination and reproducibility, and includes two framing phases that address setup and documentation. Each research step is mapped across three operational domains: 1. LLM, referring to tasks delegated to language models; 2. Mind, referring to conceptual and interpretive contributions by the historian; and 3. Computational, referring to conventional programming-based methods like Python, R, Cytoscape, etc. The study emphasizes that LLMs are not replacements for domain expertise but can support and expand capacity of historians to process, verify, and interpret large corpora of texts. At the same time, it highlights the necessity of rigorous quality control, cross-checking outputs, and maintaining scholarly standards. Drawing from an in-depth study of three Shanghai merchants, the paper also proposes a structured workflow based on a real case study hat articulates the cognitive labor of the historian with both computational tools and generative AI. This paper makes both a methodological and epistemological contribution by showing how AI can be responsibly incorporated into historical research through transparent and reproducible workflows. It is intended as a practical guide and critical reflection for historians facing the increasingly complex landscape of AI-enhanced scholarship.