User Perceptions of an LLM-Based Chatbot for Cognitive Reappraisal of Stress: Feasibility Study
King Center on Global Development, Stanford University（斯坦福大学全球发展国王中心）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Microsoft Research（微软研究院）；School of Interactive Computing, Georgia Institute of Technology（佐治亚理工学院互动计算学院）
Ananya Bhattacharjee, Jina Suh, Mohit Chandra, Javier Hernandez
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.
Comparing the Impact of Pedagogy-Informed Custom and General-Purpose GAI Chatbots on Students' Science Problem-Solving Processes and Performance Using Heterogeneous Interaction Network Analysis
Faculty of Education, The University of Hong Kong（香港大学教育学院）
