<![CDATA[This study critically examines the paradigm shift in educational research methodology in the digital era by elucidating the epistemological and methodological integration of Artificial Intelligence (AI) across the stages of data collection and analysis. Employing a qualitative conceptual approach with a systematic library research design, this study synthesizes contemporary national and international scholarly literature through descriptive reflective analysis. Data were analyzed using an integrated framework combining content analysis and AI assisted thematic analysis to identify dominant methodological trends, conceptual frameworks, and emerging ethical concerns associated with AI-driven educational research. The findings reveal a fundamental transformation from linear, researcher centered methodologies toward a hybrid, data-driven paradigm characterized by intelligent analytics and algorithmic support. AI integration substantially enhances analytical efficiency, precision, and scalability, while simultaneously redefining the researcher’s role as an epistemic authority responsible for contextual interpretation, methodological reflexivity, and validation of algorithmic outputs. Nonetheless, the study highlights persistent challenges related to algorithmic bias, transparency, and ethical accountability, underscoring the necessity of human in the loop mechanisms to safeguard scientific rigor and contextual validity. This research contributes to the advancement of educational research methodology by proposing an AI-augmented epistemological framework that reconciles computational intelligence with human reflexivity, thereby reinforcing the integrity, adaptability, and scholarly relevance of educational research in the digital age.]]>
