Total Quality Management (TQM), centered on continuous improvement, customer orientation, and system optimization, has long been recognized as a key framework for optimizing educational processes and outcomes. The integrated application of both in higher education is expected to fundamentally transform vocational evaluation systems, teaching models, and student learning effectiveness. Despite continuous attention from educational research to TQM and GenAI, there remains a lack of systematic bibliometric analysis to track the developmental trajectory of their integration. This study therefore employs bibliometric methods, based on 373 articles extracted from the Web of Science (WoS) database between 2016-2026, to comprehensively utilize network analysis for identifying collaborative clusters and influential scholars, time-series analysis of annual research output for revealing development trends, and thematic content analysis of keyword frequencies for exploring research hotspots.The findings indicate that: co-citation network analysis identifies unique clusters and key figures shaping the integrated development of TQM and GenAI in vocational evaluation and student learning outcomes; time-series analysis reveals a significant surge in research output over the past two years (2022 to present), reflecting the field’s increasing maturity; co-occurrence analysis demonstrates that academia focuses on exploring the synergistic effects of TQM principles and various GenAI models on vocational skills assessment and student learning outcomes, while also delving into ethics, data privacy, and quality assurance issues, with “Total Quality Management,” “Generative Artificial Intelligence,” “Vocational Evaluation,” and “Learning Outcomes” serving as core keywords. Building upon this, the study further analyzes the collaboration patterns and thematic characteristics of TQM-GenAI integration, systematically investigates the comprehensive impact on students’ cognitive, affective, and vocational learning outcomes, and reveals their underlying mechanisms.
