Application of AI in engineering education: A bibliometric study

Abstract The integration of artificial intelligence (AI) into engineering education is essential for fostering innovation, strategic thinking and interdisciplinary skills in the intelligent era. On this basis, this study aims to track and visually represent the research outputs associated with AI applications in engineering education, providing insights into the current research landscape and identifying areas for further investigation. The analysis offers theoretical and methodological direction for leveraging AI in engineering education. Utilising bibliometric methods, we conducted a comprehensive visualisation analysis of 378 core publications from the Web of Science (WoS) database, spanning from the beginning of the twenty‐first century to the present. Our findings show a consistent rise in publication volume from 2000 to 2017, with a significant surge from 2018 to 2023. The study identifies the International Journal of Engineering Education and Computer Applications in Engineering Education as pivotal journals in the field. The research clusters around two central themes: essential supportive technologies and specific educational applications. Within engineering education, expert systems, data mining, prediction and machine learning are highlighted as key research areas. The field has evolved through distinct phases, starting with an early focus on technology support systems, moving to an emphasis on pedagogical applications, and currently striving for a balance between diverse technologies and practical applications. Context and implications Rationale for the study Research on AI‐enabled engineering education is necessary because it is a vital measure for cultivating innovative, strategic and interdisciplinary talents in the era of intelligence. The article applies bibliometric techniques to visualise the developmental pulse of AI use in engineering education. Why the new findings matter The new findings firstly help researchers to grasp the development pulse and research priorities in the field and fill the research gaps, and secondly provide theoretical and methodological guidance for the application of artificial intelligence in engineering education through visualisation. Implications for researchers and practitioners The study summarises the current situation of AI in engineering education, breaks down the knowledge map of the field, provides practitioners, especially new researchers, with important references and guidance to understand the development of the field, and triggers the continuous attention of all sectors of society to the field. In addition, school administrators will be able to guide the development and practice of education and teaching based on the findings of the study, and frontline teachers will be able to practise the integration and application of AI and engineering education, which will effectively improve the learning efficiency of engineering education.