User Perceptions of an LLM-Based Chatbot for Cognitive Reappraisal of Stress: Feasibility Study

2026-01-02

arXiv.org

AI Summary

This study addresses the limitations of existing digital mental health tools, which often rely on rigid scripts and struggle to support users in naturally articulating and cognitively reappraising stressful events. The authors developed and evaluated a single-session AI intervention powered by GPT-4o that guides working professionals through structured dialogues to reframe workplace stressors. A multimodal assessment framework—integrating a RoBERTa-based sentiment classifier, an LLM-derived stress scorer, and thematic analysis—was employed to evaluate outcomes. Deployed in a real-world workplace setting, this work demonstrates for the first time the feasibility of LLM-driven cognitive reappraisal, significantly reducing perceived stress intensity and improving coping mindsets. Automated analyses revealed a progressive decline in negative affect throughout the dialogue, and participants acknowledged the value of guided reflection, though they also noted the interaction’s scripted feel and excessive length, highlighting tensions between AI-emulated empathy and interaction design.

Technology Category

• Humans and AI: AI for Accessibility
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• cognitive reappraisal
• digital mental health
• LLM-based chatbot
• workplace stress
• user perception

Innovation

• LLM-based chatbot
• cognitive reappraisal
• digital mental health
• single-session intervention
• stress regulation