Teaching Fluid Mechanics: Pedagogical Strategies & Insights from Bibliometric Analysis

The rapid emergence and integration of Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized curriculum design globally across Higher Education Institutions (HEIs), shaped by the Fifth Industrial Revolution (Education 5.0). This has also impacted the fields of fluid mechanics/dynamics, where scientific research today continues to be heavily reliant on AI/ML-assisted computational methods. HEIs must rapidly adapt the Education 5.0 (E.D. 5.0) and Society 5.0 global vision; thus, various teaching modalities (in-person/online/hybrid) and learning strategies (active/experiential/project-based / inquiry-based) have been recently implemented across various undergraduate and graduate-level courses. Via a systematic, comprehensive bibliometric analysis implemented on the Scopus database, with appropriate Inclusion & Exclusion criteria from 2014 – 2024 (which coincides with the rise of AI /ML in the last decade), we successfully (i) identify which modalities and pedagogical strategies work best to catalyze student learning/retention, (ii) capture temporal publication trends in fluid mechanics/dynamics (that can predict future teaching/research patterns), and (iii) identify areas and countries in STEM (Science, Technology, Engineering & Mathematics) leading these initiatives. Insights and best practices obtained from this analysis can be proactively integrated into course content to ensure a more meaningful, successful course delivery in this subject domain.