Fitting the Message to the Moment: Designing Calendar-Aware Stress Messaging with Large Language Models
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
Existing stress-management tools fail to account for the timing and
contextual specificity of students' daily lives, often providing static or
misaligned support. Digital calendars contain rich, personal indicators of
upcoming responsibilities, yet this data is rarely leveraged for adaptive
wellbeing interventions. In this short paper, we explore how large language
models (LLMs) might use digital calendar data to deliver timely and
personalized stress support. We conducted a one-week study with eight
university students using a functional technology probe that generated daily
stress-management messages based on participants' calendar events. Through
semi-structured interviews and thematic analysis, we found that participants
valued interventions that prioritized stressful events and adopted a concise,
but colloquial tone. These findings reveal key design implications for
LLM-based stress-management tools, including the need for structured
questioning and tone calibration to foster relevance and trust.
