ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917596927885312 |
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| author | Sun, Lu Chan, Aaron Chang, Yun Seo Dow, Steven P. |
| author_facet | Sun, Lu Chan, Aaron Chang, Yun Seo Dow, Steven P. |
| contents | Peer review is a cornerstone of science. Research communities conduct peer reviews to assess contributions and to improve the overall quality of science work. Every year, new community members are recruited as peer reviewers for the first time. How could technology help novices adhere to their community's practices and standards for peer reviewing? To better understand peer review practices and challenges, we conducted a formative study with 10 novices and 10 experts. We found that many experts adopt a workflow of annotating, note-taking, and synthesizing notes into well-justified reviews that align with community standards. Novices lack timely guidance on how to read and assess submissions and how to structure paper reviews. To support the peer review process, we developed ReviewFlow -- an AI-driven workflow that scaffolds novices with contextual reflections to critique and annotate submissions, in-situ knowledge support to assess novelty, and notes-to-outline synthesis to help align peer reviews with community expectations. In a within-subjects experiment, 16 inexperienced reviewers wrote reviews in two conditions: using ReviewFlow and using a baseline environment with minimal guidance. With ReviewFlow, participants produced more comprehensive reviews, identifying more pros and cons. While participants appreciated the streamlined process support from ReviewFlow, they also expressed concerns about using AI as part of the scientific review process. We discuss the implications of using AI to scaffold the peer review process on scientific work and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03530 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing Sun, Lu Chan, Aaron Chang, Yun Seo Dow, Steven P. Human-Computer Interaction Peer review is a cornerstone of science. Research communities conduct peer reviews to assess contributions and to improve the overall quality of science work. Every year, new community members are recruited as peer reviewers for the first time. How could technology help novices adhere to their community's practices and standards for peer reviewing? To better understand peer review practices and challenges, we conducted a formative study with 10 novices and 10 experts. We found that many experts adopt a workflow of annotating, note-taking, and synthesizing notes into well-justified reviews that align with community standards. Novices lack timely guidance on how to read and assess submissions and how to structure paper reviews. To support the peer review process, we developed ReviewFlow -- an AI-driven workflow that scaffolds novices with contextual reflections to critique and annotate submissions, in-situ knowledge support to assess novelty, and notes-to-outline synthesis to help align peer reviews with community expectations. In a within-subjects experiment, 16 inexperienced reviewers wrote reviews in two conditions: using ReviewFlow and using a baseline environment with minimal guidance. With ReviewFlow, participants produced more comprehensive reviews, identifying more pros and cons. While participants appreciated the streamlined process support from ReviewFlow, they also expressed concerns about using AI as part of the scientific review process. We discuss the implications of using AI to scaffold the peer review process on scientific work and beyond. |
| title | ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.03530 |