Events

Adoption of artificial intelligence (AI) by authors has accelerated production of academic articles and increased submission rates to journals, thereby straining review capacity and hurting journal outcome metrics, such as turnaround time and decision accuracy. Academic journals face an imperative to improve review quality and productivity by incorporating generative AI tools in the review workflow. As a concrete first step toward this idea, we outline a particular workflow that deploys large language models as a structured, trained, frontline reviewer. The workflow underlines that AI-produced evaluations must be transparent and contestable by authors. We emphasize the need for AI-infused peer review to be journal specific, designed for efficiency, accuracy, and accountability, within a human-in-the-loop oversight framework. We build a mathematical model of journal operations to compare outcomes under a status quo no-AI workflow and the proposed AI-infused workflow. The model captures how governed AI reconfigures human reviewer effort and illuminates conditions under which the AI-infused workflow can improve vital metrics, such as turnaround time and decision accuracy. Our essential point is that journals need to adopt a deliberate and institutionally governed approach for using AI in the review process. We recognize that identifying an “optimal” AI-infused peer review workflow or even definitively projecting the consequences of one will require substantial experimentation to calibrate and configure the workflow across multiple alternative designs.