Data Saturation in Digital Qualitative Interviews: A Critical Review of Influencing Factors and Methodological Rigor in Educational Research

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Data saturation remains a fundamental yet heavily debated methodological principle in qualitative inquiry due to persistent subjectivism and methodological laxity, a challenge that has intensified with the contemporary transition toward digital and virtual interview modalities. This study examines and contextualizes the critical factors influencing data saturation when digital qualitative interviews are deployed within educational research. Utilizing a critical literature review design, a thematic analysis was conducted on 18 international peer-reviewed articles published between 2010 and 2025 to systematically identify the determinants of data sufficiency in interview-based data collection. The synthesis revealed six primary factors that dictate methodological rigor within virtual educational research environments, including data quality and thickness, the scope and complexity of the research, sampling strategies, the design of research questions and interview guides, the digital-pedagogical skills of the interviewer, and the homogeneity or diversity of the participants. Ultimately, this paper contributes a robust methodological control framework that enables educational researchers to validly operationalize data adequacy indicators, thereby mitigating the risk of premature data saturation when evaluating interactive learning systems and digital educational environments.