Bridging Traditional and AI-Enhanced Methods in Qualitative Political Research: Strategies for Using Generative AI in Coding and Analysis

The proposed paper addresses a crucial methodological challenge in contemporary political research: how to effectively integrate generative artificial intelligence (genAI) into qualitative data analysis while maintaining methodological rigor. As political science grapples with increasingly abundant textual data, from policy documents to interview transcripts, researchers need new tools to process and analyze this information effectively. The paper presents a systematic framework for incorporating large language models (LLMs) into qualitative coding processes, distinguishing between inductive and deductive approaches. Drawing from experimental applications and methodological testing, we demonstrate how genAI can support both exploratory analysis of single documents and systematic coding of larger datasets, while identifying key limitations and potential pitfalls. The paper offers specific strategies for maintaining research quality through human oversight and validation, addressing critical concerns about reliability and transparency in AI-assisted analysis. Our findings suggest that genAI can serve as a powerful complementary tool in qualitative research, particularly valuable for initial data exploration and systematic coding of large text corpora. The paper contributes to broader methodological discussions about adapting traditional qualitative methods to contemporary technological capabilities while preserving the depth and nuance characteristic of qualitative inquiry. These insights are particularly relevant for political researchers working with complex textual data, offering practical guidance for bridging classic and contemporary methodological approaches.