While AI shows promise for enhancing the efficiency of qualitative analysis, the unique human-AI interaction resulting from varied coding strategies makes it challenging to develop a trustworthy AI-assisted qualitative coding system (AIQCs) that supports coding tasks effectively. We bridge this gap by exploring the impact of varying coding strategies on user trust and reliance on AI. We conducted a mixed-methods split-plot 3x3 study, involving 30 participants, and a follow-up study with 6 participants, exploring varying text selection and code length in the use of our AIQCs system for qualitative analysis. Our results indicate that qualitative open coding should be conceptualized as a series of distinct subtasks, each with differing levels of complexity, and therefore, should be given tailored design considerations. We further observed a discrepancy between perceived and behavioral measures, and emphasized the potential challenges of under- and over-reliance on AIQCs systems. Additional design implications were also proposed for consideration.

翻译：尽管人工智能在提升定性分析效率方面展现出潜力，但不同编码策略导致的人机交互差异使得开发有效支持编码任务的可信AI辅助定性编码系统（AIQCs）面临挑战。本研究通过探索不同编码策略对用户信任与依赖AI的影响来弥合这一差距。我们采用混合方法析因实验设计，开展了包含30名参与者的3×3裂区研究及6名参与者的追踪实验，探究在AIQCs系统进行定性分析时，文本选择范围与编码长度的变化效应。结果表明，定性开放式编码应被概念化为一系列复杂度各异的子任务，因而需要针对性的设计考量。我们进一步观察到感知测量与行为测量之间的偏差，并强调了对AIQCs系统产生低度或过度依赖的潜在风险。研究最后提出了额外的设计启示供学界参考。