MindfulAgents: Personalizing Mindfulness Meditation via an Expert-Aligned Multi-Agent System

TL;DR

MindfulAgents, a multi-agent system powered by large language models that generates guided meditation scripts based on an expert-established mindfulness framework, encourages users'lection on emotional states and mindfulness skills, and enables real-time personalization of the mindfulness meditation experience for each user is presented.

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). Furthermore, a four-week deployment study (N=62) demonstrated a notable increase in long-term engagement (p = 0.002) and level of mindfulness (p = 0.023). Participants reported that MindfulAgents offered more relevant meditation sessions personalized to individual needs in various contexts, supporting sustained practice. Our findings highlight the potential of LLM-driven personalization for enhancing user engagement in digital mindfulness meditation interventions.

Figures (9)

• Figure 1: MindfulAgents, a multi-agent system that provides expert-aligned, engaging, and personalized mindfulness meditation experience. The multi-agent system consists of: (A) an Expert-Alignment Agent that utilizes expert knowledge and feedback in the creation of safety meditation templates. (B) a Reflection Agent that encourages users to ponder their current emotional state, past meditation experiences, and technique-related questions, and (C) a Personalization Agent that builds on both the safety templates and reflective inputs to generate sessions that are user-specific and resonant.
• Figure 2: LLM Training and Retrieval Pipelines for Each Agent. Left) For the Expert-Alignment Agent, mindfulness practice curriculum scripts and UM concepts are used for supervised finetuning (SFT). Experts' edits and feedback on the intermediate generated scripts are included as additional data to further tune the model via both SFT and direct preference optimization (DPO); Middle) For the Reflection Agent, meditation Q&A dialogues between instructors and students are used for SFT to emulate reflective conversations. And UM concepts are used for retrieval-augmented generation (RAG); Right) For the Personalization Agent, the SFT and DPO pipeline is similar to that of the Expert-Alignment Agent, except that standard curriculum scripts and mindfulness concepts are no longer included to better focus on goal-oriented scripts during the finetuning. Meanwhile, UM concepts are used for RAG to ensure content reliability.
• Figure 3: The User Flow across MindfulAgents Interface. (A) The user starts by sharing their mood, goals, as well as preferred mindfulness technique, duration, and level of guidance. (B) The user then has the option to interact with the Reflection Agent to share their thoughts and experience. (C) The user could read the tip and personal summary cards while waiting for the personalized meditation content and audio being generated. (D) The user enters the meditation experience with an audio player and reports feedback at the end of the meditation session.
• Figure 4: Overview of the Formative Lab Study and Field Deployment Study. (A) Formative Study used a within-subject design (N=13) to compare user experience across three ablation conditions: StaticAgent (MindfulAgents without personalization or reflection), PersonalAgents (MindfulAgents without reflection), and the full MindfulAgents system. (B) Deployment Study used a between-subjects design (N=62) to compare StaticAgent and MindfulAgents over a four-week in-the-wild deployment, focusing on long-term engagement and changes in mindfulness and broader well-being.
• Figure 5: Formative Study Results. (A) Scores across five user experience metrics demonstrate that MindfulAgents has the overall best performance. Error bars indicate standard error. (B) Preference rankings show consistent findings. MindfulAgents received the highest ranking overall, followed by PersonalAgents, and then StaticAgent.