Reconstructive Social Research Prompting (RSRP). Distributed Interpretation between AI and Researchers in Qualitative Research

This article explores the intersection of artificial intelligence (AI) and reconstructive qualitative social research, posing how AI can support complex interpretation processes in this field. The proposed method, Reconstructive Social Research Prompting (RSRP), aims to leverage AI to interpret empirical materials that are intersubjectively verifiable. The paper outlines the distinctive characteristics of qualitative social research and its differentiation from quantitative methods, emphasizing the importance of basic and object theories, methodologies, and methods. Chapter 2 discusses these dimensions, highlighting the conceptual distinctions and the importance of theoretical and empirical components. Chapter 3 introduces the creation of RSRP prompts, focusing on dynamic practices like chain-of-thought and train-of-thought prompting to guide AI in interpreting qualitative data. The article presents an architecture for these prompts, detailing the iterative process between the researcher and AI to refine interpretations. Chapter 4 theorizes concepts by presenting an example from a current research project. This example demonstrates the practical application of RSRP in analyzing group discussions, showcasing how AI can generate meaningful interpretations. In conclusion, this article underscores the transformative potential of RSRP for the qualitative research community. It highlights how this method can significantly enhance the efficiency and depth of qualitative analysis and emphasizes its practical benefits. The article suggests that through collaborative and iterative processes, AI can evolve into a partner in qualitative research, challenging traditional notions of intelligence and interpretation and paving the way for more insightful research.