A Bibliometric Analysis of Computer Science Research at the University of Information Technology, VNU-HCM: Trends, AI Alignment, and Institutional Benchmarking (2010–2025)

We conduct a multi-dimensional bibliometric analysis of Computer Science (CS) research at the University of Information Technology (UIT), a specialized IT institution in Vietnam, to examine AI adoption, thematic evolution, and institutional benchmarking over 2010–2025. The analysis is based on 1,703 Scopus-indexed CS publications affiliated with UIT during this period. Five analytical dimensions are applied: (i) publication trend and growth rate; (ii) thematic evolution across six rule-based clusters; (iii) citation impact quadrant analysis; (iv) a proposed Global AI Alignment Score A(T) = 12/15 = 0.80, measuring UIT’s research coverage of 15 AI/CS trends curated from recent top-tier venues; and (v) co-authorship network analysis. Key findings include: annual output tripled post-2020, driven by an AI/ML cluster CAGR of 44.8%; UIT exhibits the highest AI/ML publication density (34.2%) among three VNU-HCM peer institutions—HCMUS-CS (1,944 records, 23.6%) and HCMUT-CS (2,114 records, 19.3%)—queried with the same method; and NMF topic modeling (k = 6) independently recovers five of six predefined clusters (agreement 34.1% vs. 16.7% random baseline, p < 0.001), validating the rule-based taxonomy. Self-supervised learning (3 occurrences) and medical AI (18 occurrences) are identified as the most actionable research gaps relative to global trends. The citation gap between UIT (mean 6.3/paper) and peer institutions is largely associated with journal submission rates rather than research quality. The framework and A(T) indicator provide a transferable tool for institution-level AI trend monitoring and can be replicated for other specialized technology universities. An open-source reproducible pipeline is released to facilitate annual benchmarking for any Scopus-indexed institution.