Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol.2

Purpose: This work presents a new Artificial Intelligence (AI) method to support decision-making in selecting optimal articles from the user's perspective. The technique leads to selecting high-quality works that fit the researcher's needs by enabling hierarchizing scientific publications based on their similarity to a predefined researcher problem. This article is the first in a planned series on AI-powered systematic literature reviews. Need for the Study: The rapid development of AI over the last few years has caused the number of publications to grow rapidly and their quality to decrease. Developing a filtering method to select adequate papers is imperative in an AI-dominated environment where classic methods are no longer effective. Methodology: The embedding model transforms research queries and publication metadata into numerical vector representations. The cosine similarity metric ranks publications based on their semantic closeness to the research query. Next, bibliometric analyses validate the effectiveness of the selection process. Findings: The proposed AI-powered selection method significantly improves the thematic consistency of selected publications compared to entire datasets downloaded from the Scopus database. Selected articles exhibit higher bibliometric similarity in shared references, keywords, and co-citations. The approach efficiently filters large datasets while maintaining relevance, providing researchers with a refined subset of highly pertinent publications. Practical Implications: The proposed method can improve systematic literature reviews by automating the selection of relevant publications, reducing manual workload, and ensuring thematic accuracy. The findings suggest that AI-driven text embedding techniques can enhance the efficiency and reliability of literature filtering, benefiting researchers across various disciplines.