Topic modeling has become a widely used unsupervised machine learning method for extracting latent themes from large textual datasets. However, the interpretability of these themes often relies heavily on human judgment, which can limit transparency and reproducibility. Recent advances in large language models (LLMs) and prompt engineering offer new opportunities to enhance the interpretability and scalability of topic modeling outputs. This study presents a hybrid, human-in-the-loop methodological framework that integrates topic modeling, LLM prompting, and human-derived codes to support rigorous qualitative analysis. We apply this framework to focus group interviews with 13 U.S. teachers discussing the conceptualization and assessment of communication and digital literacy skills within competency-based education (CBE) contexts. The multi-stage process includes semantic clustering, LLM-assisted topic labeling, and iterative codebook refinement, enabling both scale and interpretive depth. Our findings demonstrate that this approach supports construct alignment, thematic stability, and methodological transparency, while preserving the contextual richness of qualitative data. We also highlight the importance of human oversight in guiding LLM outputs and ensuring theoretical coherence. This work contributes to emerging best practices for integrating AI tools into qualitative educational research by offering a replicable approach for analyzing complex, open-ended data that maintains both scalability and interpretability. The framework demonstrates how computational tools can augment human interpretive expertise while maintaining the epistemological integrity essential to qualitative inquiry. Supplemental materials are available at: https://doi.org/10.17605/osf.io/4q6w8.
Ober et al. (Sun,) studied this question.