A New Model for Qualitative Research: Connecting Triangulation, Crystallization, and Artificial Intelligence

Abstract

This study examines the methodological transition from triangulation to crystallization and investigates the emerging role of artificial intelligence (AI) in qualitative research. The central research question addressed whether traditional approaches, such as triangulation and crystallization, are sufficient to capture the complexity of meaning-making in the digital era. Findings suggest that, while triangulation enhances data validity, it tends to favor convergence and may overlook the polyphony of data. Crystallization embraces diversity and contradictions, providing a richer portrayal of phenomena, yet faces challenges when confronted with the vast volume of digital data. To address these limitations, this research proposes an innovative model that incorporates AI as an “algorithmic co-analyst” within the qualitative research process. The model creatively integrates triangulation, crystallization, and algorithmic analysis, enabling the detection of hidden patterns, amplification of contradictions, and improved scalability of qualitative inquiry. The primary contribution of this study lies in presenting a multi-paradigmatic framework that preserves methodological rigor, deepens interpretive richness, and leverages technological capacities to grasp the complexity of the digital world better. This approach opens a new horizon for the future of qualitative research, demonstrating that the integration of humans, data, and algorithms provides an effective pathway for studying multilayered and dynamic social phenomena.

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