Algorithmic Interviewers and the Reflexivity of Knowledge Production: Toward an Intelligent Critical Methodology in AI-Based Qualitative Research

Document Type : علمی - پژوهشی

Authors

1 Professor, Department of Social Sciences, University of Tabriz, Tabriz, Iran

2 Assistant Professor, Department of Sport Management, University of Tabriz, Tabriz, Iran

Abstract

Extended Abstract
 
Introduction and Objectives: The rapid evolution of artificial intelligence (AI) technologies has made the rethinking of knowledge production methods an undeniable necessity. AI, as a swiftly advancing field, is poised to transform the very foundations of research methodology. One of the most remarkable manifestations of this transformation is the emergence of algorithmic interviewers—tools that, relying on natural language processing algorithms, establish quasi-human interactions with participants.
AI language models are recognized for their extraordinary ability to write academic papers, design experiments, develop theories, transcribe, translate, conduct thematic analysis, code qualitative data, summarize articles, recommend journals, guide researchers, complete sentences and paragraphs, and generate human-like text. These capabilities make them invaluable resources for qualitative researchers. However, their entry into research is not merely a technological advancement; it entails a redefinition of the relationships among humans, machines, power, and knowledge.
Given the growing difficulty of conducting contemporary qualitative research and data analysis in the humanities and social sciences without computational tools, the adoption of modern technologies in computer-assisted qualitative data analysis (CAQDAS) reshapes interpretive frameworks and transforms our understanding of research phenomena. The aim of this paper is to critically re-examine the relationship between AI and knowledge production in qualitative research—with a particular focus on algorithmic interviewers and their potential to disrupt or reproduce epistemic and social relations.
Drawing on Michel Foucault’s power-knowledge theory and Manuel Castells’ network society theory, this study proposes a methodological model termed “Intelligent Critical Methodology”—a framework that enables researchers to develop both theoretical and practical sensitivity toward the biases, limitations, and ethical-political dimensions of AI-based research tools.
Method: This study investigates the role of algorithmic interviewers in redefining social relations and power structures in qualitative research. The research was conducted in two stages: a comparative-documentary phase and an intelligent critical phase.
In the first phase, Foucault’s and Castells’ theories were synthesized to develop a theoretical framework for analyzing AI-driven interviews. Foucault’s perspective aids in examining the power dynamics embedded in algorithmic tools, while large language models (LLMs) such as ChatGPT reveal new sources of epistemic power.
The second phase focused on developing the Intelligent Critical Methodology, which integrates AI technologies with human interpretation. This hybrid methodology enhances analytical transparency and precision, reduces analysis time, and simultaneously strengthens data validity through human interpretive oversight—thereby mitigating the biases inherent in purely human or purely algorithmic analyses.
To assess the validity of this indigenous Intelligent Critical Methodology, the study examined its alignment with power-knowledge and network-society theories. Triangulation through combined human–algorithmic analysis, joint coding, and participant review of findings contributed to the study’s credibility and dependability.
Ultimately, the research designed an indigenous intelligent critical framework that allows researchers to simultaneously leverage human experience and algorithmic processing power for deeper insights into digital-era social interactions. This approach underscores the importance of algorithmic transparency and data literacy, urging researchers to act as creative co-producers of knowledge in collaboration with AI.
Findings: The findings indicate that large language models such as ChatGPT can substantially enrich qualitative research by identifying latent patterns, formulating questions, transcribing, translating, coding, developing theory, and producing coherent text. However, these models are not neutral tools—they function as epistemic agents capable of influencing discursive structures and interpretive directions.
They can also detect social and cultural patterns, offering deeper insight into power relations. One key outcome of this study is the recognition that algorithmic interviewers can generate transparent and well-documented datasets, enabling researchers to trace analytic processes and substantiate results more effectively. This feature enhances transparency and trustworthiness in qualitative research, facilitating more precise analysis of complex data.
Moreover, integrating algorithmic and human analysis helps reduce bias and improve accuracy—particularly in sociocultural contexts where emotional and contextual interpretation is critical. This hybrid approach represents a paradigm shift in how qualitative data are understood and interpreted.
Finally, the study emphasizes the need to rethink research methodologies and design new approaches to address the ethical and social challenges of AI use in research. The findings provide a foundation for developing research and educational policies on AI-assisted qualitative inquiry, contributing to the improvement of research quality and validity in the social sciences.
Discussion and Conclusion: Recent advances in AI and data-mining technologies have profoundly influenced qualitative and social research methods. This study explored the impact of algorithmic interviewers on qualitative research, particularly regarding social relations and power–knowledge structures.
The results show that large language models (LLMs) such as ChatGPT function not merely as facilitative tools but as epistemic actors that participate in data interpretation and meaning-making. Such transformations also reshape social structures and human relationships.
Foucault’s theory highlights the pervasive role of power in everyday interactions, while Castells conceptualizes information as a new source of power. Together, these perspectives illuminate how AI reconfigures social relations and epistemic authority.
The study concludes that a critical and informed use of AI tools can enhance the precision and coherence of qualitative analyses—especially in complex phases like transcription and thematic analysis. The proposed indigenous intelligent critical methodology enables researchers to combine human interpretation with algorithmic processing, transforming them into creative co-producers of knowledge.
Ultimately, the research underscores the necessity of redefining the researcher’s role and designing new frameworks for documenting human–machine interactions, emphasizing critical and responsible approaches to AI use in social research.

Keywords

References

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