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Scientific article review platform using generative artificial intelligence to streamline the peer review process
Abstract
This study introduces a novel Generative Artificial Intelligence (GAI) platform designed to streamline the peer review process. By analyzing a case study of 10 scientific articles, we demonstrate that GAI effectively evaluates article quality and pinpoints specific areas requiring improvement. Our platform achieves an average similarity of 63.6% with human reviewers, enabling the automation of routine evaluation tasks while enhancing both efficiency and objectivity. By drawing on recent generative AI benchmarks across research support, educational assessments, reviewer matching, and large-scale application studies, we demonstrate a focused, practically validated solution that not only aligns with but slightly outperforms general GAI performance levels, offering a transformative approach to real-world manuscript evaluation.
Keywords
Project Number
19506.24-PD
References
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Details
Primary Language
English
Subjects
Measurement and Evaluation in Education (Other)
Journal Section
Research Article
Authors
Early Pub Date
October 1, 2025
Publication Date
December 5, 2025
Submission Date
November 22, 2024
Acceptance Date
August 1, 2025
Published in Issue
Year 2025 Volume: 12 Number: 4