Education and Employment of Women in Software, Computer, and Artificial Intelligence Engineering: Trends and Thematic Structures in the Academic Literature

Abstract

This study presents a bibliometric analysis of the scientific literature on the employment and education of women in engineering. A total of 220 records retrieved from the Web of Science Core Collection were analyzed in terms of document types, keyword co-occurrence, author productivity, source distribution, and conceptual density. VOSviewer, RStudio (Bibliometrix), and Gephi were used for network visualization, thematic mapping, and clustering analysis. The most frequently used keywords were gender, software engineering, and education, while the most influential sources were identified using Bradford’s Law. Lotka’s Law was applied to evaluate author productivity. The study systematically reveals thematic trends, collaborative structures, and key focus areas in the literature, providing a strategic basis for future research on women's representation in engineering education and employment.