Preprint: “AI and the Future of Academic Peer Review”

The preprint shared below was recently posted on arXiv.

Title

AI and the Future of Academic Peer Review

Authors

Sebastian Porsdam Mann
University of Copenhagen

Mateo Aboy
Joel Jiehao Seah
Zhicheng Lin
Xufei Luo
Dan Rodger
Hazem Zohny
Timo Minssen
Julian Savulescu
Brian D. Earp

Source

via arXiv
DOI: 10.48550/arXiv.2509.14189

Abstract

Peer review remains the central quality-control mechanism of science, yet its ability to fulfill this role is increasingly strained. Empirical studies document serious shortcomings: long publication delays, escalating reviewer burden concentrated on a small minority of scholars, inconsistent quality and low inter-reviewer agreement, and systematic biases by gender, language, and institutional prestige. Decades of human-centered reforms have yielded only marginal improvements. Meanwhile, artificial intelligence, especially large language models (LLMs), is being piloted across the peer-review pipeline by journals, funders, and individual reviewers. Early studies suggest that AI assistance can produce reviews comparable in quality to humans, accelerate reviewer selection and feedback, and reduce certain biases, but also raise distinctive concerns about hallucination, confidentiality, gaming, novelty recognition, and loss of trust. In this paper, we map the aims and persistent failure modes of peer review to specific LLM applications and systematically analyze the objections they raise alongside safeguards that could make their use acceptable. Drawing on emerging evidence, we show that targeted, supervised LLM assistance can plausibly improve error detection, timeliness, and reviewer workload without displacing human judgment. We highlight advanced architectures, including fine-tuned, retrieval-augmented, and multi-agent systems, that may enable more reliable, auditable, and interdisciplinary review. We argue that ethical and practical considerations are not peripheral but constitutive: the legitimacy of AI-assisted peer review depends on governance choices as much as technical capacity. The path forward is neither uncritical adoption nor reflexive rejection, but carefully scoped pilots with explicit evaluation metrics, transparency, and accountability.

Direct to Abstract + Link to Full Text

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