Peer Review Week 2025 is here
This year’s theme: ‘rethinking peer review in the AI era’.
As the scientific publishing landscape evolves, so too must the systems that support it. We recognise the transformative potential of artificial intelligence in peer review, such as the opportunities to enhance efficiency, reduce bias, and support reviewers and editors alike. However, we also believe that the integrity, transparency and human judgement at the heart of peer review must be preserved. As part of Peer Review Week 2025, we are reflecting on how AI can responsibly complement 鈥� not replace 鈥� the critical role of expert reviewers in maintaining the quality and trustworthiness of scientific research.
For this year鈥檚 Peer Review Week theme, Rethinking Peer Review in the AI Era, we asked Energy Advances editor-in-chief, Volker Presser, some thought-provoking questions around AI usage and how he sees its place in the long-term landscape of peer review.
If AI simply accelerates the pressure on the same pool of overburdened reviewers, we solve nothing. Used wisely, AI can open doors to overlooked experts and create more balanced workloads 鈥� but only if inclusivity and transparency are built into the system from the start.
Volker Presser, editor-in-chief, Energy Advances
Can AI improve the efficiency of reviewer selection, and could this alleviate reviewer fatigue?
Yes, AI can significantly streamline reviewer selection - especially in large, interdisciplinary fields where manually matching manuscripts to the right experts can be slow and uneven. Intelligent algorithms can parse topic relevance, past publication patterns and availability indicators more rapidly than any editorial office could. However, we must be cautious: reviewer fatigue isn't just a logistical problem, it's also a cultural one. If AI simply accelerates the pressure on the same pool of overburdened reviewers, we solve nothing. Used wisely, AI can open doors to overlooked experts and create more balanced workloads - but only if inclusivity and transparency are built into the system from the start.
Can AI improve inclusivity in peer review by helping to reduce bias in the reviewer selection process?
Potentially, yes 鈥� but it depends entirely on the data and the design. An AI trained on past reviewer behaviour may inadvertently replicate existing biases, such as favoring reviewers from well-funded institutions or established networks. On the other hand, if used thoughtfully, AI can help diversify reviewer pools by surfacing names from underrepresented regions, institutions or career stages that human editors might otherwise overlook. The key is intentionality: AI won鈥檛 鈥渁utomatically鈥� be more inclusive - it has to be programmed and guided that way.
How can AI enhance peer review without eroding the human values at its core?
Peer review at its best is a deeply human exchange - one based on expertise, critical thought and an underlying respect for intellectual effort. AI can assist with mechanical aspects, such as detecting plagiarism, data inconsistencies or assessing statistical robustness, but it cannot replace scholarly judgment, contextual nuance or projected impact. We must ensure that AI enhances the process, not automates it into irrelevance. Reviewers aren鈥檛 just validators - they are co-creators of scientific quality. That ethos must be preserved.
How might the level of transparency and disclosure required when AI is involved in reviewing or authoring scholarly content evolve?
Transparency will become a central pillar of trust in this new era. Authors using AI in writing or data analysis should clearly disclose its role, just as we expect for experimental tools or statistical methods. Likewise, if reviewers or editors use AI tools, readers and authors deserve to know when and how these tools influenced the process. Disclosure is not about punishment - it鈥檚 about accountability and shared understanding. As with all disruptive technologies, the challenge is to establish norms before opacity becomes the default.
What can be done to ensure that editors, reviewers and researchers have the AI literacy needed to navigate this new normal?
We need structured, field-specific training 鈥� and soon. AI literacy shouldn't be an optional skill for early career researchers or something that editors pick up on the side. Scientific publishers, institutions and societies must jointly take responsibility to provide practical guidance on how and when AI tools can be ethically used. Just as we train researchers to avoid statistical misuse or plagiarism, we must now equip them to critically evaluate AI outputs and understand their limitations. Empowerment starts with education.
What guardrails must be in place to ensure fairness, reproducibility and trust?
Guardrails must be both technical and ethical. Technically, we need audit trails and human-in-the-loop systems that ensure editorial oversight at every step. Ethically, we must resist the temptation to 鈥渄elegate judgment鈥� to AI. Models change, data shifts and the logic of machine predictions is often opaque. Trust in science depends on traceability and reproducibility 鈥� both of which are threatened when we allow AI to act without clear checks. Peer review must remain a human responsibility, augmented - but not replaced - by machines.
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