In the current big data era, qualitative coding faces the challenge of balancing scalability and contextual depth. This study proposes a novel computer-assisted code generation framework that integrates generative artificial intelligence (GAI), stepwise coding, and topic modeling to enhance transparency and traceability in inductive analysis. Unlike prior work assuming full human coding, our approach compresses data using GAI to extract representative utterances, which are then analyzed via the Steps for Coding and Theorization (SCAT), a stepwise coding method. We compared three topic modeling techniques—latent Dirichlet allocation (LDA), biterm topic model (BTM), and BERTopic—using raw and SCAT-processed data. The results show that BTM applied to stepwise-coded data yields the most interpretable and thematically relevant topics. Coding tables constructed from BTM topics enabled epistemic network analysis (ENA) that visualized meaningful pedagogical perspective shifts before and after a technology trial. The findings suggest that the proposed hybrid approach can maintain analytical depth while supporting scalable qualitative analysis. This framework advances code generation practices in quantitative ethnography by preserving the cultural and interpretive context of human-centered inquiry.