Qualitative research, the kind that dives into messy human experiences and perspectives, has always been a distinctly human endeavor. But what if artificial intelligence could lend a hand? A new research paper proposes a framework for thinking about how large language models (LLMs), like the ones powering ChatGPT, could be integrated into qualitative analysis. The core question isn't *whether* AI can replace human researchers – it can't – but *how* these powerful tools can augment our existing methods. The framework revolves around two key questions: 'Is the LLM proposing or refuting a qualitative model?' and 'Is the human researcher checking the LLM’s work?' This focuses attention on the crucial role of human oversight and emphasizes that LLMs should be assisting, not automating, the process of making sense of complex data. One particularly promising application highlighted in the paper is using LLMs to find counterexamples within massive datasets. Imagine you're analyzing interviews about people's experiences with healthcare. An LLM could sift through thousands of responses, flagging instances that challenge emerging themes or assumptions. This allows researchers to refine their understanding, ensuring they aren't missing crucial nuances or alternative perspectives. This 'counter-example' approach offers common ground for researchers with differing philosophical stances on qualitative analysis, fostering collaboration and the development of innovative research practices. While the future of AI in qualitative research is still unfolding, this framework provides a valuable roadmap for navigating the exciting possibilities and potential pitfalls. It encourages us to view LLMs not as replacements for human insight, but as powerful tools that can expand our capacity to understand the human experience.