Imagine an article about peer review that didn’t follow the usual tropes. One that didn’t open with the quote that like democracy it is imperfect, but the best system we have. It wouldn’t close summarising that until a better system is found, we would do well to understand its strengths and weaknesses.

If peer review is to be fair and accountable, strong governance is needed, and fast. Researchers should know if AI is evaluating their work and the role it plays in decisions. Governance should address: who is responsible for AI errors; how authors can challenge AI-based decisions; and, what level of explanation must systems provide for their assessments?

For decades, the scholarly publishing ecosystem has relied on a loose federation of publishers, libraries, and indexing services to manage trust. Retraction Watch, Crossref, PubMed, OpenAlex, and others have all done heroic work, but their authority is partial, unevenly implemented, and not designed for machine governance. Their signals are advisory rather than binding. Their metadata is often optional. And their integration into AI pipelines is inconsistent or nonexistent.

The result is a system in which every actor defines “truth,” “trust,” and “reliability” differently. That cannot work in an AI-driven research environment.

The crisis in peer review does not represent a system fail- ure, but rather reflects a model designed for a pre-digital era. Artificial intelligence does not replace reviewers but enables them to focus on the most intellectually relevant tasks. By automating repetitive processes, increasing pre- cision, and expanding participation, AI can help restore scientific evaluation’s credibility and accelerate knowl- edge advancement. The future of science does not belong exclusively to humans or machines, but to their strategic collaboration. In this new ecosystem, knowledge integrity will depend on hybrid systems capable of responding re- sponsibly to the challenges of the information age.

The rapid expansion of AI research has intensified the Reviewer Gap, threatening the peer-review sustainability and perpetuating a cycle of low-quality evaluations. This position paper critiques existing LLM approaches that automatically generate reviews and argues for a paradigm shift that positions LLMs as tools for assisting and educating human reviewers. We define the core principles of high-quality peer review and propose two complementary systems grounded in these foundations: (i) an LLM-assisted mentoring system that cultivates reviewers’ long-term competencies, and (ii) an LLM-assisted feedback system that helps reviewers refine the quality of their reviews. This human-centered approach aims to strengthen reviewer expertise and contribute to building a more sustainable scholarly ecosystem.