Using Generative Artificial Intelligence in Mindfulness Technology Within an Everyday Digital Context

Abstract

Chronic stress contributes to adverse physical and mental health outcomes. Regular mindfulness practice has been shown to reduce stress and improve well-being. Although digital mindfulness tools are widespread, most applications rely on pre-recorded guided content and offer limited support for monitoring users’ internal processes or evaluating practice effectiveness. To address this gap, this project presents Recalibrate , a user-centered web application that integrates generative AI, a large language model (LLM), and neural text-to-speech (TTS) to support brief, personalized mindfulness practices within everyday digital contexts. Using a research-through-design approach, the system emphasizes reduced cognitive workload through short guided meditations, breath awareness, and optional reflective journaling. A typical session begins with a mood and somatic check-in, which informs the generation of a personalized one-minute meditation script and journal prompt. The meditation is delivered through natural voice output, followed by optional journaling, intention setting, and feedback. By combining adaptive AI-generated guidance with lightweight reflective practices, Recalibrate contributes a transferable set of design considerations for creating supportive, low-demand reflective systems.