MindfulAgents: Personalizing Mindfulness Meditation via an Expert-Aligned Multi-Agent System
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911512883363840 |
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| author | Wu, Mengyuan Millie Jiang, Zhihan Fan, Yuang Feng, Richard Dharmavaram, Sahiti Polowitz, Mathew Fallon, Shawn Islam, Bashima Benson, Lizbeth Tung, Irene Creswell, David Xu, Xuhai |
| author_facet | Wu, Mengyuan Millie Jiang, Zhihan Fan, Yuang Feng, Richard Dharmavaram, Sahiti Polowitz, Mathew Fallon, Shawn Islam, Bashima Benson, Lizbeth Tung, Irene Creswell, David Xu, Xuhai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06926 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MindfulAgents: Personalizing Mindfulness Meditation via an Expert-Aligned Multi-Agent System Wu, Mengyuan Millie Jiang, Zhihan Fan, Yuang Feng, Richard Dharmavaram, Sahiti Polowitz, Mathew Fallon, Shawn Islam, Bashima Benson, Lizbeth Tung, Irene Creswell, David Xu, Xuhai Human-Computer Interaction Artificial Intelligence 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. |
| title | MindfulAgents: Personalizing Mindfulness Meditation via an Expert-Aligned Multi-Agent System |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2603.06926 |