AI 中文总结
本研究通过案例研究分析了ChatGPT-5和ScholarAI在统计研究的文献综述与代码生成中的应用，发现其需专业人员提示监督，可作为研究工具但无法替代专业方法论知识。
AI 中文摘要
生成式人工智能（GenAI）是一种大型语言模型（LLM），能够基于用户提供的提示生成媒体内容。鉴于ChatGPT等模型在信息综合与编程方面已展现出的能力，人们对其在研究过程中的潜在作用日益关注。然而，针对统计研究领域中GenAI模型用于研究任务的评估工作却很少。本案例研究将GenAI作为工具，开展文献综述工作，并将学术论文中的方法论转化为代码，研究主题为通过动态加权普通最小二乘法（dWOLS）方法进行动态治疗方案（DTR）估计。具体而言，我们利用ChatGPT-5和ScholarAI（2025年9月至11月发布版本）完成文献综述相关的相关文献识别、论文摘要撰写、研究缺口识别，以及实现该方法论的R代码生成工作。研究结果表明，当前GenAI模型缺乏完成上述任务所需的深度与上下文理解能力，除非在具备专业知识的研究人员的精心提示与监督下才能完成。不过，GenAI在利用其搜索与摘要能力、基础代码调试及算法构建的任务中，具备提升效率的潜力。我们证明，在专业指导下，GenAI可作为研究工具发挥作用，但无法替代方法论专业知识。
英文摘要
Generative artificial intelligence (GenAI) is a large language model (LLM) that has the ability to generate media based on user-provided prompts. Given the demonstrated capabilities of models such as ChatGPT in information synthesis and programming, there is growing interest in their potential role within the research process. However, little work has evaluated recent GenAI models for research tasks in the domain of statistical research. This case study examines GenAI as a tool for developing a literature review and translating methodology from academic papers into code, for the topic of dynamic treatment regime (DTR) estimation via the dynamic weighted ordinary least squares (dWOLS) approach. Specifically, we utilize ChatGPT-5 and ScholarAI (Sept-Nov 2025 release) in the processes of identifying relevant sources for the literature review, creating summaries of papers, identifying gaps in research, and R code generation to implement methodology. Our findings show that current GenAI models lack the depth and contextual understanding required to accomplish these tasks without careful prompting and supervision of a knowledgeable researcher. Nonetheless, GenAI has potential to increase efficiency of tasks which take advantage of its search and summarization abilities, as well as basic code debugging and algorithm formation. We demonstrate that under a knowledgeable guide, GenAI can function as a research tool, but not as a substitute for methodological expertise.
