The Challenges and Upsides of Using AI in Scientific Writing

This is a guest post. The views expressed here are the author’s own and do not represent positions of IEEE Spectrum, The Institute or IEEE.

Scientific writing is at a pivotal stage, driven by artificial intelligence as a disruptor and enabler. Academics, publishers, and policymakers are attempting to weigh the value of using AI responsibly to enhance productivity versus risking the integrity and purpose of scholarly communication. In this context, the responsible use of the technology in scientific writing pertains to employing AI tools in ways that uphold the integrity, transparency, and ethical standards of scholarly communication.

As we collectively contend with the challenges and define AI’s ethical use, we must ask: Is AI revolutionizing scientific writing or undermining it?

Technology has long been involved in shaping the scientific writing landscape. Word processors and then personal computers revolutionized how manuscripts were created and shared. The emergence of online submission platforms and open-access repositories further transformed access to knowledge, allowing for large-scale global collaboration and peer review. Modern methods also include alternative metrics that track and analyze the awareness an article generates online to determine where the research is having an impact on social media.

From the early digitization of research dissemination to the influence and leverage of social media, challenges to balance progress with quality in writing continue to evolve.

AI, especially generative large language models, can draft manuscripts, conduct literature reviews, provide translations, and generate content faster than humans can. The ubiquitous nature and rapid evolution of the advancements, however, require stakeholders to take a step back and consider their ethical and practical limitations and implications.

A unified effort within the academic community is needed to ensure that AI in scientific writing is used responsibly to enhance critical thinking, not replace it. This concept aligns with the broader vision of augmented artificial intelligence, advocating for the collaboration between human judgment and AI toward ethical technology development and applying the same principles to scientific writing.

Policies and frameworks must stay rooted in the fundamentals of scientific writing: advancing knowledge, prioritizing quality over quantity, and fostering transparency and accountability. Excessive use of AI can have the opposite effect and raise concerns over plagiarism and academic integrity, especially as traditional AI detection algorithms require continuous adaptation to stay relevant. Empirical research lays the foundation for identifying AI’s limitations and refining detection tools to align the technology’s capabilities with ethical standards.

Through international collaborative efforts and our shared experiences on the responsible use of AI, we will be able to develop appropriate measures to deal with it in scholarly…