Tracing four decades of research on expert systems in engineering education: A bibliometric analysis

Using the Scopus database, this study conducted a bibliometric analysis of the literature on expert systems (ES) in engineering education from 1983 to 2024. Data was retrieved from the Scopus database with a controlled search strategy on the TITLE-ABS-KEY field, resulting in 425 initial documents, filtered to 332 final documents after the PRISMA selection process. VOSviewer and Bibliometrix analysis was conducted to map collaboration networks, keyword trends, and research impact. Results showed an annual publication growth of 5.2 %, with conference papers (63.55 %) dominating journal articles (27.11 %). Keyword analysis indicated a shift from rule-based systems to artificial intelligence (AI)-based approaches, such as machine learning and deep learning. The United States (212 documents) and China (101 documents) were the main contributors, while Swansea University and the University of Fortaleza were the most productive institutions. Key challenges include the black-box nature of AI systems, the need for large-scale data, and resistance to technological change. This research highlights the potential of ES in personalizing learning while emphasizing the need for further research on AI transparency, global collaboration, and ethical frameworks for sustainable implementation.