Scientific writing: a step-by-step guide to the ethical use of AI

Tools can automate tasks and refine text, but transparency and human involvement remain the cornerstones of scientific output

Juliana Protásio

A person uses a laptop on a wooden desk in a work or study setting. The image suggests writing, academic research, or digital content creation.

“It is forbidden to forbid,” said neurologist João Brainer, a clinical researcher at Einstein Hospital Israelita and professor at the Federal University of São Paulo (UNIFESP), when discussing the use of generative artificial intelligence (AI) in scientific research.

The rapid growth of AI tools and how to use them with methodological rigor in scientific work were the focus of a virtual meeting hosted by Science Arena on May 28 to share practical tips on the ethical use of AI in producing scientific articles.

During the livestream, Brainer explained a fundamental premise: the output from AI tools reflects the quality of the human input. Providing vague instructions and data will result in a low-quality paper.

The secret lies in prompt engineering and transparency.

The guide below offers practical tips for writing scientific articles based on the experiences shared by João Brainer, who teaches a course on AI in science at Einstein.

Watch the full livestream about the use of AI in scientific writing:

Start with a compelling introduction

The introduction should contextualize the problem and capture the reader’s interest, explains Brainer. To avoid generic text, provide the AI with your preliminary data and key references.

Suggested prompt: “Write an introduction of four to five paragraphs. In the first, introduce the problem of [insert your problem here], focus on [insert your insights], and use the following references [list references]. The objective of this text is [state your purpose]. Use appropriate, objective, and clear language.”

Methodology and hallucination-free results

For Brainer, methodology is the heart of the study. “You need to follow certain steps, to know what to write at each step and how, including ethical considerations and the statistical analysis plan.” A tip to ensure scientific rigor is to use standard guidelines for your research methods.

Suggested prompt: Upload the guideline and supporting paper to your study, and ask the tool to help outline the steps of your methodology. “Ask me for detailed information on each item based on this methodology in the context of my study.” You can also use your data to ask the AI to create a statistical analysis plan tailored to the objectives of your paper.

Brainer emphasizes that to ensure AI tools check properly for inconsistencies, researchers should use emphatic terms to trigger a more in-depth review.

Suggested prompt: “Make sure that what I have written is consistent with the type of study I am conducting; identify any ambiguities or inconsistencies.”

Do not be afraid to create three, four, or five prompts on the same subtopic; multiple checks will improve the quality of your paper.

Intelligent organization of references and data

Verifying sources is the cornerstone of scientific legitimacy. Specialized tools such as Consensus, Elicit, SciSpace, and Perplexity can help survey the literature and manage references.

Visual elements also require precise descriptions.

Suggested prompt for figure/graph captions: “Create a detailed explanatory caption for this dataset [insert graph data], highlighting the variables analyzed.”

Transparency and open science

For a manuscript to be considered credible by respected academic journals, Brainer argues that authors should explicitly indicate where they used AI and what for, either in the methodology section or the acknowledgments.

Data auditing is the best way for scientists to protect themselves against accusations of plagiarism or fraud. Brainer therefore also recommends depositing raw data and the prompts used on public platforms, such as Mendeley.

AI can save time during the writing process, but critical judgment, ethical responsibility, and final validation remain unequivocally human.

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