AI-Assisted Editorial Decision Making in Open Journal Systems (OJS): A Framework for Manuscript Scope Analysis and Reviewer Recommendation
Keywords:
Artificial Intelligence, Open Journal Systems (OJS), Natural Language Processing, TF-IDF, Cosine Similarity, Manuscript Scope Analysis, Reviewer RecommendationAbstract
The increasing volume of manuscript submissions has created significant challenges for editorial boards in maintaining efficient and consistent editorial workflows. Initial manuscript screening, assessment of manuscript relevance to the journal scope, and identification of appropriate reviewers are among the most time-consuming tasks in academic publishing. Although Open Journal Systems (OJS) provides a comprehensive platform for managing editorial processes, these tasks are still performed primarily through manual evaluation.
This study presents an AI-assisted editorial support framework designed for Open Journal Systems (OJS). The proposed approach applies natural language processing techniques to analyze manuscript titles, abstracts, keywords, and reviewer expertise profiles. Textual information is transformed into numerical representations using the Term Frequency–Inverse Document Frequency (TF-IDF) vectorization method, while cosine similarity is employed to measure semantic relevance between submitted manuscripts, journal scope descriptions, and reviewer expertise. Based on these similarity scores, the system recommends the most relevant journal scope categories and suitable reviewers for each submitted manuscript.
The proposed framework is implemented as a Python-based prototype and demonstrates the feasibility of integrating artificial intelligence into editorial workflows without replacing the role of the editor. Instead, the system functions as an intelligent decision-support tool that provides transparent and consistent recommendations during the initial manuscript screening process. The proposed approach has the potential to reduce editorial workload, improve reviewer assignment, and enhance the overall efficiency of manuscript management within Open Journal Systems.
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