人工智能在科研工作中的规范边界：来自博士研究者的证据
Normative boundaries of AI in scientific work: Evidence from PhD researchers
AI总结：
该研究通过对3785名博士的调查，识别出四种AI态度群体，发现科研中AI态度围绕任务边界而非接受-拒绝划分，强调需采用任务特定的AI治理等方法。
AI中文摘要：
人工智能（AI）正日益融入科研工作，但研究者对其在不同研究任务中的使用评价可能并不统一。本研究分析了参与《自然》2025年研究生调查的3785名STEM、医学与健康科学领域的国际自愿博士样本，研究其对AI的任务特定态度。我们调查了受访者对AI用于撰写研究论文、收集分析数据、设计实验、追踪科学文献及总结文献的接受度。潜在类别分析识别出四种不同态度群体：占主导的“分工”群体广泛接受AI用于文献相关任务，但抵制与智力贡献紧密相关的活动，如写作、数据分析和实验设计；“现状”群体在所有任务中普遍不适；“全能”群体普遍接受；“未决定”群体则表现出显著不确定性。这些模式表明，科研领域对AI的态度并非围绕简单的接受-拒绝划分，而是围绕任务特定边界，可能涉及授权、署名和责任问题。由于调查测量的是接受度而非合法性，这些群体最好被解读为具有规范维度的态度组合。研究结果强调了任务特定方法在AI治理、博士培训、披露及研究评估中的重要性。
英文摘要：
Artificial intelligence (AI) is increasingly embedded in scientific work, but researchers may not evaluate its use uniformly across research tasks. This study examines task-specific attitudes towards AI among an international, self-selected sample of 3,785 PhD students in STEM and medical and health sciences who participated in Nature's Graduate Survey 2025. We analyse respondents' comfort with using AI for writing a research article, collecting and analysing data, designing experiments, tracking scientific literature, and summarising it. Latent class analysis identifies four distinct attitudinal profiles. The dominant profile reflects a "division of labour," in which AI is widely accepted for literature-related tasks but resisted in activities closely associated with intellectual contribution, such as writing, data analysis, and experimental design. A "status quo" profile is broadly uncomfortable across tasks, an "all-purpose" profile is broadly comfortable, and an "undecided" profile expresses substantial uncertainty. These patterns suggest that attitudes towards AI in research are organised less around a simple acceptance-rejection divide than around task-specific boundaries, likely concerning delegation, authorship, and responsibility. Because the survey measures comfort rather than legitimacy, the profiles are best interpreted as attitudinal configurations with a normative dimension. The findings highlight the importance of task-specific approaches to AI governance, doctoral training, disclosure, and research evaluation.
