Cognitive reappraisal is a well-studied emotion regulation strategy that helps individuals reinterpret stressful situations to reduce their impact. Many digital mental health tools struggle to support this process because rigid scripts fail to accommodate how users naturally describe stressors. This study examined the feasibility of an LLM-based single-session intervention (SSI) for workplace stress reappraisal. We assessed short-term changes in stress-related outcomes and examined design tensions during use. We conducted a feasibility study with 100 employees at a large technology company who completed a structured cognitive reappraisal session delivered by a GPT-4o-based chatbot. Pre-post measures included perceived stress intensity, stress mindset, perceived demand, and perceived resources. These outcomes were analyzed using paired Wilcoxon signed-rank tests with correction for multiple comparisons. We also examined sentiment and stress trajectories across conversation quartiles using two RoBERTa-based classifiers and an LLM-based stress rater. Open-ended responses were analyzed using thematic analysis. Results showed significant reductions in perceived stress intensity and significant improvements in stress mindset. Changes in perceived resources and perceived demand trended in expected directions but were not statistically significant. Automated analyses indicated consistent declines in negative sentiment and stress over the course of the interaction. Qualitative findings suggested that participants valued the structured prompts for organizing thoughts, gaining perspective, and feeling acknowledged. Participants also reported tensions around scriptedness, preferred interaction length, and reactions to AI-driven empathy. These findings highlight both the promise and the design constraints of integrating LLMs into DMH interventions for workplace settings.

翻译：认知重评是一种经过充分研究的情绪调节策略，通过帮助个体重新解读压力情境以减轻其影响。许多数字心理健康工具因采用僵化脚本而难以适应用户对压力源的自然描述，从而无法有效支持这一过程。本研究探讨了基于大语言模型（LLM）的单次干预（SSI）用于工作压力重评的可行性。我们评估了压力相关指标的短期变化，并分析了使用过程中的设计张力。我们在某大型科技公司对100名员工开展可行性研究，参与者通过基于GPT-4o的聊天机器人完成结构化认知重评会话。前后测指标包括感知压力强度、压力心态、感知需求与感知资源。采用经多重比较校正的配对Wilcoxon符号秩检验分析这些指标。同时使用两个基于RoBERTa的分类器和一个基于LLM的压力评估器，分析对话四分段中的情感与压力轨迹。开放式回答采用主题分析法进行解析。结果显示：感知压力强度显著降低，压力心态显著改善；感知资源与感知需求的变化趋势符合预期但未达统计显著性。自动化分析表明负面情感与压力水平在交互过程中持续下降。定性研究发现，参与者高度评价结构化提示在整理思路、获得新视角和感受被理解方面的价值，同时也揭示了脚本化程度、偏好交互时长以及对AI驱动共情反应的接受度等设计张力。这些发现凸显了将LLM整合至职场数字心理健康干预中的潜力与设计约束。