  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.
