Most peer reviewers now use AI, and publishing policy must keep pace
Source: Frontiers · 2025-12-15
A new whitepaper from Frontiers shows that AI has rapidly become part of everyday peer review, with 53% of reviewers now using AI tools. The findings in Unlocking AI’s untapped potential: responsible innovation in research and publishing point to a pivotal moment for research publishing. Adoption is accelerating and the opportunity now is to translate this momentum into stronger, more transparent, and more equitable research practices as demonstrated in Frontiers’ policy outlines.
Drawing on insights from 1,645 active researchers worldwide, the whitepaper identifies a global community eager to use AI confidently and responsibly. While many reviewers currently rely on AI for drafting reports or summarizing findings, the report highlights significant untapped potential for AI to support rigor, reproducibility, and deeper methodological insight.
“AI is already improving efficiency and clarity in peer review, but its greatest value lies ahead. With the right governance, transparency, and training, AI can become a powerful partner in strengthening research quality and increasing trust in the scientific record.”
The study shows broad enthusiasm for using AI more effectively, especially among early-career researchers (87% adoption) and in rapidly growing research regions such as China (77%) and Africa (66%). Researchers in all regions see clear benefits, from reducing workload to improving communication, and many express a desire for clear, consistent policy recommendations that would enable more advanced use.
In response, Frontiers has set out a series of evidence-based policy recommendations for publishers, institutions, funders, and tool developers. These include:
and ensuring equitable access to trustworthy AI tools.
Together, these recommendations provide a practical roadmap for aligning publishing policy with how researchers are already using AI and for unlocking its full potential to strengthen scientific rigor and trust.
“AI is transforming how science is written and reviewed, opening new possibilities for quality, collaboration, and global participation. This whitepaper is a call to action for the whole research ecosystem to embrace that potential. With aligned policies and responsible governance, AI will strengthen the integrity of science and accelerate discovery.”
The report encourages publishers, institutions, and policymakers to collaborate on sector-wide policy development, training pathways, and transparent communication to support responsible and innovative AI use across the research cycle.
Unlocking AI’s untapped potential: responsible innovation in research and publishing is based on a global survey of 1,645 active researchers, conducted in May and June 2025. It is the first large-scale study to examine AI adoption, trust, training, and governance within authoring, reviewing, and editorial workflows.
Lines of inquiry this paper opens 19
Research framings built by reading the notes related to this paper — the questions it feeds into.
Can AI systems perform peer review as effectively as humans?- Did adding AI reviews actually change peer review decisions or paper outcomes?
- How often do researchers violate rules about AI use in review?
- What specific tasks do reviewers use AI for most often?
- How often do researchers suspect peer reviews are written by AI?
- Do peer reviewers actually follow restrictions on using AI tools themselves?
- How fast is scientific publishing growing relative to reviewer capacity?
- Why do researchers resist using AI for peer review specifically?
- How much of ICLR 2026 peer review was already conducted by AI?
- Does AI content in reviews correlate with differences in paper quality control?
- Can automated reviewers actually handle the review load AI creates?
- Why do early-career researchers adopt AI tools at higher rates?
- Do early-career researchers adopt AI faster than senior researchers overall?
- Does AI adoption make researchers more productive but narrower in focus?
- Does AI adoption narrow the range of research questions scientists pursue?
- How do AI researchers currently estimate timelines to artificial general intelligence?
- Have AI researcher timelines shifted based on recent capability evidence?