Harnessing BERT for the Automation of Peer Review Process by Prediction of Recommendation Score

In the digital age, the surge in research paper publications poses substantial challenges. The unavailability and time constraints among peer reviewers strain efficient evaluation. Moreover, the varying evaluation patterns of each reviewer can lead to inconsistent scores, hindering uniform outcomes in paper acceptance decisions. Through this research, we embark on an insightful journey to reimagine the peer review process, a cornerstone of academic integrity and scholarship offering a visionary approach to enhancing the efficiency and objectivity of academic evaluations. This paper addresses these challenges by exploring the automation of peer reviews by recommendation score prediction using BERT (Bidirectional Encoder Representations from Transformers) in the peer review process. Leveraging the valuable insights provided by expert reviewers, the system aims to normalize the decision-making process by analyzing and classifying research papers. Patterns and trends identified from a comprehensive analysis of peer reviews serve as models for automated decision-making. Implementing a standardized evaluation model for all papers facilitates consistent assessment, reducing the impact of individual reviewer biases and variations providing fair and uniform evaluation. This work not only underscores the technological synergy possible in future educational landscapes but also addresses the unique challenges faced by developing nations in integrating cutting-edge AI tools for academic advancement. Reflecting the ethos of “Reimagining Transformative Educational Spaces”: offering a visionary approach to enhancing the efficiency and objectivity of academic evaluations.