A Modular LLM-Driven System for Managing Academic Submissions and Editorial Workflow

Abstract

In this research, an artificial intelligence based academic paper management system is developed. The major aim of the system is to assist conferences and journals in handling the entire process of academic papers from start to finish. The system offers the functionality of uploading papers, assigning users, assigning reviewers, peer review, editorial decisions, and tracking the submissions through an interactive web interface. One of the most highlighted areas in the system is the incorporation of large language models, Google Gemini, in the peer review process. The AutoReview component is used to analyze the language, structure, and originality of the submitted papers, and the Reviewer Matching component uses artificial intelligence to suggest suitable reviewers according to the semantic similarity between the manuscript information and the expertise of the reviewers. The system is developed by utilizing PHP, MySQL, and PDO for the secure back-end, and the front-end is made responsive by utilizing Bulma CSS and jQuery. The developed system is fully functional and is currently being used in a live production environment to manage real-world conference applications.

Keywords

• Artificial Intelligence
• Academic Paper Management System
• Peer Review Automation
• Large Language Models
• Reviewer Matching

Akademik Makale Gönderimi ve Editörlük İş Akışı Yönetimi için Modüler LLM Tabanlı Sistem

Abstract

Çalışmada, yapay zekâ tabanlı akademik bildiri ve makale yönetim sistemi geliştirilmiştir. Sistemin temel amacı, konferans ve dergilerin akademik makalelerin baştan sona tüm sürecini yönetmesine yardımcı olmaktır. Sistem, etkileşimli bir web arayüzü aracılığıyla makale yükleme, kullanıcı atama, hakem atama, akran değerlendirmesi, editoryal kararlar ve gönderimlerin takibi gibi işlevler sunmaktadır. Sistemin en öne çıkan özelliklerinden biri, akran değerlendirme sürecine büyük dil modelleri olan Google Gemini'nin dahil edilmesidir. AutoReview bileşeni, gönderilen makalelerin dilini, yapısını ve özgünlüğünü analiz etmek için kullanılırken, Reviewer Matching bileşeni yapay zekâ kullanarak makale bilgileri ile hakemlerin uzmanlıkları arasındaki anlamsal benzerliğe göre uygun hakemler önerir. Sistemin arka ucunda PHP, MySQL ve PDO kullanılarak güvenlikli olarak geliştirilmiş ve ön uç, Bulma CSS ve jQuery kullanılarak duyarlı hale getirilmiştir. Geliştirilen sistem tamamen işlevseldir ve konferans başvurularını yönetmek için canlı bir ortamda kullanılmaktadır.

Keywords

• Yapay Zekâ
• Akademik Makale Yönetim Sistemi
• Hakem Değerlendirme Otomasyonu
• Büyük Dil Modelleri
• Hakem Eşleştirme

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