  Qualitative analysis of textual contents unpacks rich and valuable
information by assigning labels to the data. However, this process is often
labor-intensive, particularly when working with large datasets. While recent
AI-based tools demonstrate utility, researchers may not have readily available
AI resources and expertise, let alone be challenged by the limited
generalizability of those task-specific models. In this study, we explored the
use of large language models (LLMs) in supporting deductive coding, a major
category of qualitative analysis where researchers use pre-determined codebooks
to label the data into a fixed set of codes. Instead of training task-specific
models, a pre-trained LLM could be used directly for various tasks without
fine-tuning through prompt learning. Using a curiosity-driven questions coding
task as a case study, we found, by combining GPT-3 with expert-drafted
codebooks, our proposed approach achieved fair to substantial agreements with
expert-coded results. We lay out challenges and opportunities in using LLMs to
support qualitative coding and beyond.
