Artificial intelligence is an increasingly important part of how research is created, reviewed, and shared, particularly in the context of preprints.
More researchers are choosing to share early versions of their work online. AI tools play a growing role in supporting the preparation, screening, and informal evaluation of research before formal peer review. These developments are reshaping how authors, readers, and publishers interact with preprint literature.
As AI is integrated into preprint platforms, the academic community must reconsider how early-stage research is evaluated and trusted. When used responsibly, AI can strengthen the review of preprints by reducing repetitive tasks, improving organization, and helping authors communicate their ideas more clearly.
At the same time, research credibility depends on careful judgment and critical thinking. In the preprint environment, findings are shared prior to peer review. It is thus essential to balance the speed and accessibility enabled by AI with the need for accuracy and rigor.
The challenge lies in using AI to support knowledge sharing without weakening the standards of the scientific method.
The Dos and Don’t of AI
The use of AI in preprint workflows presents both opportunities and risks. AI can assist with technical and organizational tasks. But, it cannot independently assess the originality, importance, or scientific value of research. This limitation is particularly relevant for preprints, which are shared prior to formal peer review. However, preprints are only posted after completing a rigorous screening process, with opportunities for informal feedback from the scientific community.
The Journal of Korean Medical Science (2025) notes a key limitation of AI in research assessment:
“AI lacks the subtle understanding of complex scientific content that human expertise provides,
posing challenges in evaluating research novelty and significance.”
This highlights that although AI can support research review processes, it lacks the human judgment, disciplinary experience, and ethical awareness required to fully evaluate emerging research. Human oversight, therefore, remains essential.
Preprints and AI-supported screening
Preprints are early versions of research papers made publicly available prior to formal peer review. They allow findings to be shared more quickly, encouraging discussion, collaboration, and transparency. In fast-moving fields, preprints play a crucial role in enabling timely access to new information.
Although preprints are not peer reviewed, they are still expected to follow established ethical standards and research practices. AI can assist in upholding these standards by identifying plagiarism, citation issues, and potential policy violations before or after posting.
Many preprint servers and journals currently use AI for administrative and screening purposes, such as managing submissions and detecting duplicated text or citation errors.
In addition, the use of AI for grammar correction and language editing in preprints is generally accepted when disclosed. AI can also assist with reference checking and identifying missing or outdated sources, which is especially useful prior to peer review.
Research review in the preprint context
A research review is a structured examination of existing academic literature designed to identify key findings, gaps in knowledge, and areas of agreement or disagreement within a field.
In the preprint context, this process may be supported by initial platform screening, as well as further shaped by authors, readers, and wider research community through comments and feedback. Such review does not provide formal validation. But it plays an important role in improving clarity, accuracy, and rigor before peer review.
AI tools such as ChatGPT are increasingly used to support research review activities related to preprints. These tools can assist with literature searches, summarizing large bodies of work, organizing references, and identifying gaps in existing research. They may also be used to generate summaries, reorganize sections, or revise drafts before posting a preprint.
By handling time-consuming tasks, AI can allow researchers engaging with preprints to focus more on interpretation, analysis, and critical evaluation. However, AI-generated feedback should be viewed as supportive rather than authoritative, particularly when applied to research that has not yet undergone formal review.
Integrity, authorship, and disclosure
AI tools do not qualify as authors and cannot take responsibility for the content they generate. They cannot be held ethically or legally accountable and should never replace human authorship.
Transparency is especially important in the context of preprints. Therefore, AI must remain a clearly defined support tool rather than an authority in the research process.
Any use of AI in preparing, revising, or reviewing preprints should be openly disclosed in accordance with relevant guidelines. Tools such as ChatGPT and other large language models should not be listed as authors. Instead, their use should be described in the Methods section or another appropriate part of the manuscript.
Overall, AI has the potential to strengthen research review for preprints by improving efficiency, accessibility, and consistency while supporting open science and early knowledge sharing. However, AI cannot evaluate scientific quality or significance in the way that human experts can. For this reason, human judgment must remain central to the evaluation and interpretation of preprint research.
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