Document Type : علمی - پژوهشی
Authors
1
Department of Social Sciences, University of Tabriz, Tabriz, Iran
2
Department of Sports Management, Faculty of Physical Education and Sport Sciences, University of Tabriz, Tabriz, Iran
10.30471/mssh.2025.10961.2640
Abstract
The emergence of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, has introduced new opportunities and challenges for qualitative research. Despite the increasing use of these tools, there remains a lack of systematic and critical frameworks for their integration into qualitative inquiry. This study addresses this gap by critically examining the role of algorithmic interviewers in reconfiguring social relations, epistemic power, and knowledge production processes. Drawing on Foucault’s theory of algorithmic discipline and Castells’ concept of digital networks of power, we propose a novel framework called Critical Indigenous Intelligent Methodology, which synthesizes human-centered analysis with algorithmic collaboration. Our findings demonstrate that LLMs can meaningfully enhance the richness of qualitative research at various stages including pattern recognition, question design, transcription, translation, thematic coding, theory development, and textual production. However, these models are not neutral tools; they function as epistemic agents capable of shaping discursive structures and influencing interpretive trajectories. Challenges such as algorithmic bias, limited emotional understanding, and ethical concerns related to privacy highlight the need for a cautious and reflexive engagement with such technologies. This research underscores the importance of algorithmic transparency and data literacy as essential competencies for qualitative researchers in the AI era. Ultimately, the paper calls for a paradigmatic reconsideration of qualitative methods and advocates for creative, critical, and responsible interactions with intelligent systems.
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