This study presents a structured evaluation of the methodological potential and limitations of using AI-assisted deductive coding in qualitative content analysis, drawing on newly integrated AI coding features in the qualitative data analysis software MAXQDA. Our approach takes a pragmatic stance, positioning AI coding as a promising emerging tool requiring careful methodological consideration for its effective integration into existing workflows. Focusing on six deductive categories derived from Glock’s dimensions of religiosity, as refined by Huber’s Centrality of Religiosity Scale (CRS), we examine how AI-generated codes can support traditional deductive coding practices within narrative interviews about religious turning points. Through a two-phased comparative analysis of 101 cycles of AI coding, we focused first on the inputs to AI-assisted coding, exploring how the complexity of coding instructions impact AI outputs. Second, we focused on the outputs, assessing AI-generated codes against manually coded segments by examining three distinct aspects: the overall degree to which AI and manual codes match (correspondence), how well the segments align in terms of their textual boundaries (segment scope), and the level of thematic detail they capture (code granularity). Our findings showed that highly complex coding instructions narrowly targeting the central research questions yielded the most stable results. AI-generated codes captured 64% to 66.5% of manually coded segments on average but were typically broader in scope and less fine-grained, often containing multiple codable segments. Additionally, 47.6% of AI coded segments were found not to align with manual coding. These results support a hybrid approach to AI-assisted coding of qualitative data in which AI generated codes can serve as a useful reference for human coders, but in which human coders render final judgment in the segmentation and validation of coded segments.
Schueller et al. (2026) studied this question.