AI 中文总结
研究探讨科研中使用大语言模型时研究人员所需能力，通过分析相关文章确定八项能力，指出领域专业知识是关键，培养人员使用大语言模型需综合多方面能力，结果对研究生项目和人工智能素养计划设计有指导意义。
AI 中文摘要
科学研究中对大语言模型的使用日益增加，这就需要了解研究人员和研究生批判性且负责任地使用这些工具所需的能力。本快速回顾分析了从Elicit和谷歌学术搜索（2022年至2025年）中检索到的194篇文章，经独立双筛选后（Gwet AC1：0.76至0.83），选取40篇进行能力提取和主题分析，确定了八项能力。最普遍的是领域专业知识和对人工智能输出的监督，还包括元认知和人工智能使用决策、伦理与学术诚信、研究提示工程、人工智能使用的可重复性等。人工智能素养和技术知识缺乏时是风险因素，领域专业知识是有意义的批判性评估的前提。研究结果表明，培养研究人员使用大语言模型不仅需要技术指导，还需要一套以人类对所产生知识的责任为中心的认知、伦理和方法能力，这对研究生项目和人工智能素养计划的设计有直接影响。
英文摘要
The growing adoption of Large Language Models in scientific research has created a need to understand what competencies researchers and graduate students require to use these tools critically and responsibly. This rapid review analyzed 194 articles retrieved from Elicit and Google Scholar (2022 to 2025), from which 40 were selected for competency extraction and thematic analysis following independent dual screening (Gwet AC1: 0.76 to 0.83). Eight competencies were identified. The most prevalent was domain expertise and oversight of AI outputs (n = 123), encompassing subject matter mastery, systematic skepticism, source verification, and researcher accountability. Other key competencies include metacognition and decision making about AI use (n = 55), ethics and academic integrity (n = 53), prompt engineering for research (n = 38), and reproducibility of AI use (n = 29). AI literacy and technical knowledge (n = 16) was explicitly identified as a risk factor when absent, with domain expertise treated as a prerequisite for meaningful critical evaluation. The findings suggest that preparing researchers to use LLMs goes beyond technical instruction, requiring an integrated set of epistemic, ethical, and methodological competencies centered on human accountability for the knowledge produced. These results have direct implications for the design of graduate programs and AI literacy initiatives.
