Sociology Meets AI: Rethinking Knowledge Production, Research Methodology, and Scientific Standards

Artificial intelligence (AI) is increasingly being integrated into sociological research. AI is used to, for example, support literature reviews, formulate research questions and hypotheses, develop and test instruments, and process, analyze, and interpret data. While these advances create novel opportunities for sociological knowledge production, they also raise fundamental questions about scientific integrity, transparency, accountability, validity, and reproducibility.

A key challenge is how established standards of good scientific practice can be maintained and adjusted under conditions of AI-assisted sociological research. How does the use of AI affect the quality and credibility of empirical findings? How can AI-assisted research processes be documented and made transparent and traceable? What new challenges emerge for scientific responsibility? And what methodological implications follow for the production and evaluation of sociological knowledge?

This open session invites theoretical, methodological, and empirical contributions that examine the opportunities, limitations, and risks of AI-assisted sociological research. We particularly welcome contributions that address the following topics (but not limited to them): 1) Documentation, transparency, and reproducibility of AI-assisted research outcomes. 2) Development of research questions, hypotheses, instruments, and designs. 3) Data collection and generation, as well as qualitative and quantitative data analysis. 4) Evaluation of AI-assisted research methods (e.g., data classification). 5) Quality assurance (e.g., bias and hallucinations). 6) Scientific integrity and research ethics in the context of AI (e.g., within peer review and publication processes). 7) AI in open science, research data management, and sociological training.