Mapping Integration and Triangulation in AI-Enhanced Mixed Methods Research: A Bibliometric Analysis

Abstract

This study reviews the scholarly discourse at the intersection of mixed methods research (MMR) and artificial intelligence (AI), specifically examining how MMR’s foundational commitments to methodological integration and evidence triangulation are represented across the literature while highlighting the continuing importance of human methodological judgment in AI-enabled research. A bibliometric analysis was conducted on 1,208 Scopus-indexed publications spanning 2016–2026, employing performance analysis, science mapping, and thematic mapping. Analytical tools included Bibliometrix and VOSviewer, with document selection following PRISMA-guided procedures. The findings reveal extraordinary growth, with an annual rate of 95.28% that accelerated after 2023. China (n = 273) and the United States (n = 261) emerge as leading contributors to the field. Thematic mapping demonstrates that AI occupies the conceptual core of the literature, whereas MMR functions primarily as a peripheral bridging construct. Integration and triangulation appear infrequently as distinct thematic clusters, suggesting limited explicit theorization of these foundational methodological principles. Collaboration is intensive (3.66 co-authors per publication) but modestly international (23.76%). The findings indicate that AI is currently positioned as a methodological catalyst that enhances research processes rather than a disruptive force that fundamentally redefines mixed methods inquiry. Despite increasing AI adoption, the robustness, credibility, and interpretive depth of MMR continue to depend on researchers’ capacity for critical reasoning, contextual understanding, ethical reflection, and meaningful synthesis of diverse forms of evidence – competencies that remain distinctly human and indispensable for rigorous scientific inquiry.