Qualitative analysis is a challenging, yet crucial aspect of advancing research in the field of Human-Computer Interaction (HCI). Recent studies show that large language models (LLMs) can perform qualitative coding within existing schemes, but their potential for collaborative human-LLM discovery and new insight generation in qualitative analysis is still underexplored. To bridge this gap and advance qualitative analysis by harnessing the power of LLMs, we propose CHALET, a novel methodology that leverages the human-LLM collaboration paradigm to facilitate conceptualization and empower qualitative research. The CHALET approach involves LLM-supported data collection, performing both human and LLM deductive coding to identify disagreements, and performing collaborative inductive coding on these disagreement cases to derive new conceptual insights. We validated the effectiveness of CHALET through its application to the attribution model of mental-illness stigma, uncovering implicit stigmatization themes on cognitive, emotional and behavioral dimensions. We discuss the implications for future research, methodology, and the transdisciplinary opportunities CHALET presents for the HCI community and beyond.

翻译：定性分析是推动人机交互领域研究发展的关键且具有挑战性的环节。近期研究表明，大型语言模型（LLM）能够在现有框架内执行定性编码，但其在协作发现与生成定性分析新见解方面的潜力仍待探索。为弥合这一差距并借助LLM力量推动定性分析发展，我们提出CHALET方法——一种利用人机协作范式促进概念化、赋能定性研究的新方法论。CHALET方法包含LLM支持的数据采集、通过人类与LLM演绎编码识别分歧点，以及针对分歧案例开展协作归纳编码以衍生新概念见解三个环节。我们通过将其应用于精神疾病污名归因模型验证了CHALET的有效性，揭示了认知、情感与行为维度中的隐性污名化主题。本文进一步探讨了该研究对未来研究范式、方法论的影响，以及CHALET为人机交互领域及更广泛学科带来的跨学科协作机遇。