MindfulAgents: Personalizing Mindfulness Meditation via an Expert-Aligned Multi-Agent System
Mengyuan Millie Wu, Zhihan Jiang, Yuang Fan, Richard Feng, Sahiti Dharmavaram, Mathew Polowitz, Shawn Fallon, Bashima Islam, Lizbeth Benson, Irene Tung, David Creswell, Xuhai Xu

TL;DR
MindfulAgents is a multi-agent system utilizing large language models to personalize mindfulness meditation, significantly improving user engagement, self-awareness, and stress reduction through tailored guided sessions in lab and deployment studies.
Contribution
This work introduces a novel LLM-powered multi-agent system that personalizes mindfulness meditation experiences, addressing scalability issues of manual customization.
Findings
Improved in-session engagement and self-awareness in lab study
Increased long-term engagement and mindfulness in deployment
Participants found sessions more relevant and personalized
Abstract
Mindfulness meditation is a widely accessible and evidence-based method for supporting mental health. Despite the proliferation of mindfulness meditation apps, sustaining user engagement remains a persistent challenge. Personalizing the meditation experience is a promising strategy to improve engagement, but it often requires costly and unscalable manual effort. We present MindfulAgents, a multi-agent system powered by large language models that (1) generates guided meditation scripts based on an expert-established mindfulness framework, (2) encourages users' reflection on emotional states and mindfulness skills, and (3) enables real-time personalization of the mindfulness meditation experience for each user. In a formative lab study (N=13), MindfulAgents significantly improved in-session engagement (p = 0.011) and self-awareness (p = 0.014), and reduced momentary stress (p = 0.020).…
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Taxonomy
TopicsMindfulness and Compassion Interventions · Innovative Human-Technology Interaction · Digital Mental Health Interventions
