Prompting Machines, Writing Papers: The Moral Case for Human Drafts in Science

When a researcher types a prompt into a large language model and receives a polished paragraph in return, who wrote the paper? That deceptively simple question sits at the heart of a new letter to the editor published in the Annals of Biomedical Engineering by bioethicist Timothy Daly of FLACSO Argentina and the Sorbonne, and Jaime A. Teixeira da Silva, an independent researcher in Japan. Building on a 2023 letter in the same journal that offered a practical guide to prompt engineering for academic writers, the two authors argue that the academic community has rushed to embrace prompting as a legitimate form of scholarship without ever pausing to ask whether it deserves the moral status of authorship. Their answer, delivered through three of the oldest frameworks in moral philosophy, is a firm and carefully reasoned no.

The authors structure their argument around three classical ethical traditions: consequentialism, which judges actions by their outcomes; deontology, which judges them by duties and rules; and virtue ethics, which judges them by the character of the person performing them. Each lens, they contend, reveals a different facet of the same underlying problem. A consequentialist might ask whether prompt-generated text improves or degrades the collective quality of scientific knowledge. A deontologist might ask whether researchers have a duty to be honest about how their manuscripts came into being. A virtue ethicist might ask what kind of scholar one becomes when the intellectual labor of drafting is handed to a machine. The fact that all three traditions converge on skepticism, the authors suggest, is itself a signal worth taking seriously.

The consequentialist case is complicated by genuinely mixed evidence. Experimental work published in Science in 2023 by S. Noy and W. Zhang found that generative artificial intelligence tools can substantially raise productivity in writing tasks, and a large-scale survey of German researchers reported in Research Policy in 2026 documented how widely these tools have already penetrated academic workflows. Yet a 2026 study in Nature by Q. Hao and colleagues found a troubling trade-off: artificial intelligence tools may expand the individual impact of scientists while contracting the overall focus of science, homogenizing research agendas as everyone draws on the same models. Daly and Teixeira da Silva add their own contribution to this literature with a 2026 paper defining what they call academic slop, the flood of low-quality, machine-flavored text now circulating in scholarly venues, and they point to a Lancet audit of 2.5 million biomedical papers that documented the scale of fabricated citations as evidence that the downstream harms are not hypothetical.

The deontological perspective sharpens the issue of honesty and disclosure. If a researcher presents prompt-generated prose as their own writing, they arguably violate a duty of transparency to readers, reviewers, and the scientific record. The authors note that this duty is contested terrain: some ethicists, including M. Hosseini and colleagues writing in Research Ethics in 2025, have argued that disclosure of generative AI writing assistance should be voluntary, while others, such as G. Kendall in the Journal of Academic Ethics, have proposed that authors should include both the prompts and the generated text as part of any submission. A 2025 commentary in Nature Machine Intelligence by B. D. Earp and colleagues framed the deeper problem as one of provenance: once text can be generated on demand, readers can no longer trace ideas back to the minds that supposedly produced them. Daly and Teixeira da Silva side with the camp that sees disclosure as insufficient on its own, because disclosure does not answer the prior question of whether the practice should occur at all.

Virtue ethics supplies the emotional and cultural core of their argument. On this view, the point of academic writing was never merely to produce text; it was to cultivate the intellectual virtues of patience, precision, and honest engagement with evidence. Writing a first draft is how a scientist discovers what they actually think, confronts the gaps in their reasoning, and earns the right to put their name on a manuscript. Offloading that task to a language model, the authors argue, is not a neutral efficiency gain but a loss of practice, in the same sense that a craft tradition is lost when artisans stop making things by hand. Daly has developed this theme elsewhere, calling in a 2026 paper for a kind of arts and crafts movement in the university, and in another article for a low-tech academic virtue ethics suited to the age of generative AI. The philosopher A. Ferdman has made a parallel argument in Philosophy and Technology, contending that some practices are worth preserving precisely because knowing when not to offload a task to technology is itself a skill.

Central to the letter is a pointed question: are prompters equivalent to authors, and should they be rewarded as such? The authors answer that prompting, however skillful, is not the same activity as drafting. A prompt is a request; a draft is a commitment. The person who writes a first draft takes responsibility for every claim, every citation, and every nuance of argument, while the person who prompts a model delegates that responsibility to a system that cannot bear it. This distinction matters because authorship in science is not merely a credit line; it is a guarantee that named individuals stand behind the work, can defend it, and will answer for its failures. A 2026 paper in Nature Reviews Bioengineering by Earp and colleagues compressed the point into a memorable phrase: thinking is not only writing, but writing is how scientists think.

The authors anticipate a powerful objection. Insisting on human drafting, critics say, would disadvantage researchers who do not speak English as a first language, since language models can flatten linguistic barriers that have long excluded talented scientists from Anglophone journals. Daly and Teixeira da Silva respond in a note to their letter that this concern has been overtaken by technology: drafting in one’s native language and then translating to English, whether through DeepL or a chatbot, is now an instantaneous, one-click process. The choice, they argue, is therefore not between human writing and global participation, but between writing one’s own draft in any language and outsourcing the intellectual work itself. Translation assistance and idea generation are, on their account, morally distinct from having a machine compose the substance of a manuscript.

The letter also engages with the practical messiness of enforcement. AI detection tools have proven unreliable, with a 2026 analysis by P. Tsigaris and Teixeira da Silva in Next Research arguing that most AI detector findings are false positives or false negatives, a conclusion with serious implications for researchers wrongly accused of misconduct. The authors themselves have previously argued in these pages that detection is the responsibility of editors and publishers, and their new letter implicitly shifts the burden away from detection and toward prevention: if the culture of academia reaffirms the value of the human draft, the arms race between detectors and generators becomes less central. They also cite the GAIDeT taxonomy, developed by Y. Suchikova, N. Tsybuliak, Teixeira da Silva, and S. Nazarovets, which offers researchers a structured way to decide which tasks may legitimately be delegated to generative AI and which must remain human.

What emerges from the letter is not a Luddite manifesto but a defense of a specific craft at a specific moment of vulnerability. The authors acknowledge that large language models can simplify knowledge creation and that human authors who use them are not villains; the question they press is whether the reward structures of academia, in which authorship confers credit, careers, and trust, should extend to those whose primary contribution is a well-crafted prompt. Their answer preserves a line that many journals and funding agencies are still struggling to draw. Notably, the letter itself carries a declaration that no AI was used by either author for conceptualization, drafting, editing, or validation, a small but pointed demonstration that the practice they defend remains entirely feasible. As generative models grow more capable and more embedded in every stage of research, the letter stands as a reminder that the deepest questions they raise are not technical at all, but ancient ones about outcomes, duties, and the kind of people scholars choose to be.

Subject of Research: The ethics of prompting large language models in academic publishing

Article Title: The Ethics of Prompting Large Language Models in Academic Publishing

Article References: The Ethics of Prompting Large Language Models in Academic Publishing. (n.d.). https://doi.org/10.1007/s10439-026-04356-7

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04356-7

Keywords: large language models, academic publishing, prompt engineering, research ethics, consequentialism, deontology, virtue ethics, authorship, generative AI, academic integrity, scientific writing, AI disclosure