In this project we focus on alleviating the peer review crisis in academia. With an increasing number of submissions and a limited number of reviewers, reviewers are becoming overburdened, leading to delays in the review process due to a shortage of reviewers. This research explores using AI tools, such as ChatGPT, to support reviewers by reducing their workload and improving efficiency without compromising the quality of reviews.
The project involves qualitative studies to understand the needs and demands of both reviewers and authors. The initial study examined how reviewers conduct peer reviews, identifying key challenges including iterative interactions with authors, learning how to review, review complexity, lack of training, and workload issues. Another study explored authors’ requirements and attitudes, emphasizing the need for good feedback, optimism about AI, concerns about trust, ethical considerations, and the impact of AI on review processes.
The research also includes an experiment using AI (ChatGPT) to reduce reviewers’ workload and enhance review quality. Reviewers perceived both benefits and challenges, such as saving time, providing different perspectives, and improving confidence, while also facing issues like initial setup time and integration with current workflows. The project underscores the necessity of effective collaboration workflows and robust data protection measures for implementing AI tools in the review process.
People
This project is being developed by Shiping Chen, under the supervision of Prof Anna Cox and Prof Duncan Brumby.
Publications
S Chen, DP Brumby, AL Cox (2024) How to Alleviate the Peer Review Crisis: Insights from an interview study CHIWORK2024
S Chen, DP Brumby, AL Cox (2023) How to Alleviate the Peer Review Crisis: Insights from an interview study CHI 2023 workshop “In2Writing: Intelligent and Interactive Writing Assistants”
