Jennie C. De Gagne, PhD, DNP, RN, NPD-BC, CNE, ANEF, FAAN
Writer’s Camp Guest Counselor
Artificial intelligence is here to stay.
Artificial intelligence (AI) is no longer a futuristic notion in academia; it is an active participant in how scholarship is created, reviewed, and shared. This became clear to me while listening to Professor Roger Watson, Editor-in-Chief of Nurse Education in Practice, and a Counselor here at Writer’s Camp, during an AI Nurses Network webinar1 in 2025. With three decades of experience in publishing, Watson spoke candidly about the accelerating role of AI in academic writing and peer review. This essay reflects on his insights, focusing on AI’s role in streamlining publication workflows, the evolving balance between human judgment and AI capability, the ethical challenges of authorship, and the governance responsibilities that institutions must address. The central point is clear: AI is here to stay, and our task is not to reject it but to integrate it responsibly into our workflows.
Artificial Intelligence as a Revolutionary Force in Publication Workflows
Publishing has always been slow. Even the most efficient journals struggle with delays caused by manuscript submission and peer review. Watson highlighted how AI is already streamlining these processes.
AI speeds up technical checks that once consumed hours. Formatting requirements can now be verified instantly. More substantively, AI can review adherence to reporting standards such as PRISMA for systematic reviews or CONSORT for randomized controlled trials. These checks save time while strengthening transparency and reproducibility.
AI also reduces the administrative burden of editors. Instead of manually composing dozens of letters to authors, AI can automate communication, generating reminders for revisions or structured feedback at scale. These efficiencies free editors to concentrate on intellectual content rather than clerical tasks.
AI expands the analytical reach of journals through bibliometric tools. Citation tracking, keyword mapping, and impact analytics are now produced instantly. Editors gain sharper insight into research trends, while authors benefit from faster, data-informed publishing environments. Finally, scholars themselves benefit from AI support. Non-English speakers can draft more fluently with translation tools. Researchers can summarize large amounts of literature in minutes, saving time. AI is no longer a backstage assistant; it is becoming integral to scholarly life.
The Debate on Human Judgment vs. AI Capability
These efficiencies raise a deeper question: how many humans are still required in the publishing process? The prevailing view, as Watson described it, is that AI cannot replace human judgment in evaluating novelty and significance. Peer review, we assume, depends on expertise and discernment. But does it always? Watson cited cases in which AI successfully ranked research proposals and analyzed manuscripts with results nearly identical to human reviewers. Properly trained, AI may even reduce the biases that cloud human judgment.
At the same time, biases can be embedded in algorithms, reflecting the limitations of training data. The promise of impartiality is fragile. Yet AI can already assist in the mechanics of peer review: detecting plagiarism, screening for methodological fit, and matching manuscripts with reviewers. These are significant tasks. If AI assumes part of this responsibility, the role of human editors may shift from gatekeeping to stewardship. The central frontier is not whether AI will replace human judgment but how human judgment will evolve when partnered with intelligent systems.
Ethical Challenges: Authorship and Guidelines for Generative Content
Of all the ethical challenges, none is more contested than authorship. Should AI be acknowledged as an author when it generates content? Publishers agree the answer is no. Watson recalled the controversy that erupted when a colleague published an editorial openly admitting the use of ChatGPT. Critics condemned the move, yet paradoxically the piece became Elsevier’s most-cited editorial because of the debate it provoked. The episode reflects the tension at the heart of AI authorship. Generative AI can draft coherent text in seconds, but it is notorious for fabricating references or “hallucinating” facts, which is especially troubling in science. AI also has no accountability; it cannot defend arguments or correct mistakes.
Publishers have responded with clear guidelines. Elsevier, for example, allows AI only to improve readability and language, not to generate original content. AI cannot be credited as an author or cited as a source. Human authors must declare which AI tools were used, for what purpose, and accept full responsibility for the final work. Accountability remains with humans. As AI systems improve, distinguishing machine-generated text from human writing will become increasingly difficult. This underscores the importance of transparency, honesty, and trust in scholarly communities.
For new nurses entering scholarship, Watson’s advice was clear: do not fear AI, but learn to use it responsibly. I would add that even experienced authors must continue developing their skills as AI becomes further integrated into research. Professional development is no longer optional; it is essential.
Conclusion: The Inevitable Future of Artificial Intelligence
Looking ahead, the implications are profound. AI promises to transform systematic reviews by automating the extraction and synthesis of study data. If current trajectories hold, the very architecture of scholarly research will be reshaped around AI capacities. As Watson observed, AI is here to stay and will soon be central to scholarly practice. Efforts to ban it are futile; the technology is too advanced, too accessible, and too integrated into daily work. The more urgent task is to accommodate it responsibly.
AI may suggest, summarize, or calculate, but only scholars can interpret, critique, and ethically situate knowledge. Human oversight is not optional; it is the foundation of academic integrity. Watson’s talk highlighted both the inevitability and the opportunity before us. AI will accelerate workflows, broaden access, and reshape scholarly publishing, but it will also require us to reaffirm what cannot be delegated: judgment, accountability, and the courage to author our own ideas.
As I often say, the emergence of AI allows us to rethink what it means to be human, perhaps even to become more human. The real revolution may not be technological but human. It is a call to embrace new tools while holding fast to the values that sustain scholarship.
Reference
1. Watson R. Using AI in academic publishing in nursing. YouTube. Published April 25, 2025. Accessed October 2, 2025. https://youtu.be/L2xlNPgq50Q?si=pRGKkXYoNP01KHmU
Author’s note: I serve as an advisor on the board of the AI Nurses Network (https://www.ai-nurses.com), where I have had the privilege of working with Professor Watson. This reflection represents my own perspective and interpretation of his talk.
Disclosure: DukeGPT was used to check grammar, improve readability, and refine flow. The author retains sole responsibility for the content and interpretation.
About the Author: Jennie C. De Gagne, PhD, DNP, RN, NPD-BC, CNE, ANEF, FAAN is Clinical Professor and Director of the Nursing Education Specialty at the Duke University School of Nursing, Durham, North Carolina. She is a globally recognized scholar in cybercivility, digital ethics, and technology-enhanced learning in nursing education, with more than 200 publications and over 150 invited presentations. Her work advances how nursing faculty design student-centered online learning environments, integrate generative AI tools responsibly, and support the development of professional identities for emerging scholars. https://orcid.org/0000-0001-9814-5942; jennie.degagne@duke.edu
Author: Jennie C. De Gagne
Reviewed and Edited by: Leslie H. Nicoll
Copyright © 2025 Writer’s Camp and Jennie C. De Gagne CC-BY-ND 4.0
Citation: De Gagne JC. Artificial Intelligence and Academic Writing: Innovation with Integrity. The Writer’s Camp Journal, 2025; 1(3):14. doi:10.5281/zenodo.17714284
Very insightful post. I know for myself, as I dive deeper into professional development and higher education, AI has been very helpful for summarizing scholarly articles that would normally take me HOURS to read! However, the point you made about interpretation and critique is fundamental to the stories we craft within the scientific community.