Normative boundaries of AI in scientific work: Evidence from PhD researchers

Francesco Angelini and Johan Lyrvall

Papers from arXiv.org

Abstract: 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.

Date: 2026-08
References: Add references at CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2608.25678 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2608.25678

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().