Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future
A comprehensive survey examines how large language models can assist or automate peer review processes across academia, synthesizing techniques for review generation, post-review tasks, and evaluation methods. The research catalogs datasets and modeling approaches while addressing ethical concerns and practical implementation challenges for integrating AI into scholarly publishing workflows.
This survey addresses a critical intersection between AI capability advancement and institutional infrastructure modernization. As academic publishing faces mounting pressure from increased submission volumes and reviewer burnout, LLMs present both opportunities and risks for automating or augmenting peer review—one of academia's most labor-intensive gatekeeping mechanisms. The research systematically maps the technical landscape, covering fine-tuning strategies, reinforcement learning approaches, and agent-based systems designed to generate reviews, rebuttals, and meta-reviews with meaningful accuracy.
The broader context reflects how LLMs are reshaping knowledge validation systems. Academic peer review has remained largely unchanged for decades despite technological transformation elsewhere, making it a natural target for AI optimization. However, this automation raises fundamental questions about quality assurance, bias propagation, and the role of human judgment in scientific consensus-building.
The survey's emphasis on evaluation methodologies—spanning human-centered assessments, reference-based comparisons, and LLM-based metrics—signals that the field recognizes simple metrics cannot capture review quality's multidimensional nature. Institutions implementing AI-assisted review systems must balance efficiency gains against the risk of lower-quality feedback reaching authors, potentially degrading manuscript quality at the source.
Looking forward, adoption will likely follow a hybrid model where AI handles preliminary screening and standardized feedback generation while human reviewers focus on novel contributions and methodological soundness. The research's attention to ethical concerns and limitations suggests the community recognizes that automating peer review requires stakeholder consensus around acceptable trade-offs between speed and rigor.
- →LLMs can assist multiple peer review pipeline stages including generation, rebuttal, meta-review, and revision processes with varying effectiveness levels
- →Evaluation methods for AI-generated reviews must combine human assessment, reference-based metrics, and aspect-oriented analysis rather than relying on single evaluation approaches
- →Ethical concerns around bias, quality degradation, and institutional accountability remain unresolved challenges for deploying AI peer review systems at scale
- →Hybrid human-AI review models appear more viable than full automation for maintaining scientific quality while improving processing efficiency
- →Standardized datasets and benchmarks are essential infrastructure gaps that need addressing before widespread adoption of AI-assisted peer review