Smoking cessation remains a persistent public health challenge, with digital interventions increasingly adopted to support behavior change. This study explores how smokers plan and engage with preparatory activities suggested by conversational agents in online cessation programs. It addresses three main questions: the factors that influence engagement with these activities, the ability of large language models to identify smokers’ articulated plans, and the role of conditional "if-then" formulations in expressing coping strategies. A thematic analysis was conducted on qualitative user responses using both manual coding and automated labeling by local large language models. The findings show that smokers create context-sensitive plans shaped by emotional states, routines, and perceived usefulness of the suggestions, often expressed through conditional intentions. While manual analysis produced consistent and detailed themes, the large language model showed low agreement, highlighting limitations in current AI driven qualitative analysis. These results inform the design of more adaptive digital cessation tools and contribute to the understanding of AI’s role in supporting thematic research.