AI Should Assist Scientists, Not Replace Them, New Editorial Argues

Artificial intelligence has swept into laboratories, libraries, and lecture halls with a speed that few scientific institutions were prepared for, and the question of where machines should stop and human minds should begin has become one of the most consequential debates in modern research. A new editorial published in PNAS Nexus on 8 September 2026 takes a firm position in that debate. Written by Charles Branas and Bruce Levine, the piece proposes a set of norms for the use of AI in science, built around a single guiding principle: augmentation, not abdication. Researchers, the authors argue, should embrace AI as a tool that extends human capability, while refusing to hand over the intellectual, creative, and ethical core of science to algorithms.

The editorial draws a careful distinction between two fundamentally different categories of AI use. The first involves non-generative tasks, such as data classification, pattern sorting, and copyediting. In these applications, AI functions much like a calculator or a statistical software package: it processes information according to defined procedures, and a human remains responsible for interpreting the results. The authors judge this kind of use to be broadly acceptable. It accelerates work that would otherwise consume countless hours, and it does so without displacing the judgment that makes research meaningful. A tool that cleans up prose or organizes a dataset does not decide what a study means or whether its conclusions are trustworthy.

The second category is far more troubling in the authors’ view. When researchers turn to generative AI to produce scientific ideas, seed research questions, or draft the interpretive substance of a manuscript, the machine stops being an instrument and starts being a substitute. The editorial argues that this kind of use deserves substantially more scrutiny than it currently receives, and that it should be disclosed in scientific manuscripts so that readers, reviewers, and the wider community can evaluate how much of the work originated in a human mind. Transparency, in this framework, is not a bureaucratic burden but a precondition for trust.

The authors go further than mere disclosure. They propose that certain uses of generative AI should be disallowed outright. Specifically, they identify the core intellectual, creative, ethical, interpretive, and accountability-bearing work of research as territory where AI must not replace human interpretation and judgment. A scientific paper is not merely a collection of sentences; it is a chain of responsibility that runs from the initial question through the gathering of evidence to the final claim about what that evidence shows. If any link in that chain is forged by a system that cannot be held accountable, the entire structure is weakened. Responsibility, the editorial insists, cannot be delegated to software.

Among the specific prohibitions the authors propose are some that may surprise researchers who have already begun experimenting with AI brainstorming tools. They argue that scientists should not use AI to generate their own initial research ideas or research questions. The origin of a study, they contend, should be a human act, rooted in curiosity, observation, and lived scientific experience. Likewise, they argue that ethical determinations, risk assessments, and policy recommendations should never be outsourced to generative systems. These are precisely the tasks where errors carry the greatest consequences for people, communities, and the environment, and where the absence of human accountability would be most damaging.

One of the most striking arguments in the editorial concerns serendipity, the happy accidents that have repeatedly driven scientific progress. The authors warn that if every researcher relies on the same AI tools with similar inputs, the diversity of thought that produces unexpected discoveries will shrink. Generative models are trained to produce probable outputs based on known patterns in existing data. They are, by design, engines of the average and the expected. Science, by contrast, has often advanced by departing from prior assumptions and breaking free of typical patterns. Penicillin, cosmic microwave background radiation, and countless other breakthroughs emerged not from the most probable answer but from an anomaly that a human noticed and refused to ignore. A research ecosystem optimized for probable outputs is an ecosystem primed to miss exactly those anomalies.

This concern leads the authors to a broader warning about optimization. Science, they argue, should not be unthinkingly optimized as if it were a manufacturing workflow producing standardized outputs. A factory benefits from consistency, repeatability, and the elimination of variation. Science requires the opposite: a wide diversity of inputs, approaches, and ideas. When the same models, trained on the same corpora, are applied across millions of research projects, the risk is a homogenization of the scientific imagination itself. Questions that fall outside the statistical grain of the training data become less likely to be asked at all, not because they are unimportant but because no human curiosity set them in motion.

The editorial does not call for a retreat from technology. Its authors are careful to preserve a role for AI that is genuinely augmentative. Non-generative applications, from classifying large datasets to polishing language, can free researchers to spend more time on the work that only they can do: conceiving questions, designing studies, weighing evidence, and taking responsibility for conclusions. The problem arises not from computation but from substitution, not from assistance but from abdication. In this sense, the proposed norms are less a restriction on AI than a defense of the human role that gives AI its value in the first place.

The authors distill their argument into a clear standard for the scientific enterprise. According to the editorial, the originating research question and the final evidentiary judgment in every scientific publication should be produced by human minds. Everything between those two poles may, with appropriate care and disclosure, involve machine assistance. But the beginning and the end of the scientific process, the spark of inquiry and the verdict on the evidence, must remain human. This framing offers journals, institutions, and funding agencies a practical benchmark against which policies on AI use can be measured, and it gives individual researchers a simple test to apply to their own workflows.

As generative AI becomes more capable and more deeply embedded in the research infrastructure, debates of this kind are likely to intensify. The PNAS Nexus editorial contributes a principled starting point: AI should amplify human science, not quietly absorb it. By distinguishing acceptable non-generative uses from the generative applications that demand scrutiny and disclosure, and by drawing firm lines around idea generation, ethical judgment, and accountability, Branas and Levine offer the scientific community a framework for preserving what makes research trustworthy. The message to researchers is unambiguous. Use the tools to work faster and see further, but never hand over the questions, the judgment, or the responsibility that define science itself.

Subject of Research: Norms for the appropriate use of artificial intelligence in scientific research

Article Title: Augmentation not abdication, when using AI in research

Article References: Augmentation not abdication, when using AI in research. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, generative AI, research ethics, scientific publishing, PNAS Nexus, authorship, serendipity, research norms, AI disclosure, human-led science, accountability, scientific method