Governing AI at the clinical threshold: a grounded theory framework for responsible AI deployment in mindfulness-based mental health
Steve Haberlin, Amanda Evans, Erin Grainger, Allyson Berrios de Gacharna
Ai & Society August 29, 2026 DOI: 10.1007/s00146-026-03341-x (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Qualitative study Peer reviewed |
|---|---|
| Sample size | 14 |
| Population | Expert mindfulness clinicians and teachers representing six contemplative and clinical traditions, with 8 to 60 years of experience |
| Topics | Meditation |
| Key findings | The authors propose the Bounded Convergence Framework, a three-zone governance model that assigns irreducibly human clinical functions to an inner zone, practitioner-governed threshold conditions to a boundary zone, and legitimate AI-assisted functions to an outer zone. They identify 16 governance principles, with particular implications for contraindication screening, crisis escalation architecture, AI identity disclosure, and protection of adolescent and trauma-exposed populations. |
Abstract
AI-powered platforms for mental health delivery are scaling rapidly into contexts previously governed by trained human practitioners—without empirically grounded frameworks to guide their ethical deployment, manage adverse effects risk, or address the structural design failures that make them harmful to vulnerable populations. This constructivist grounded theory study examined how expert mindfulness clinicians and teachers conceptualize governance failures, societal harms, and design risks in AI-mediated mental health delivery. Fourteen practitioners—representing six contemplative and clinical traditions, with experience ranging from 8 to 60 years—were recruited via purposive and theoretical sampling and interviewed using a semi-structured protocol. Data were analyzed through iterative open, axial, and selective coding by a four-member team, with theoretical saturation confirmed at N = 13. Analysis generated the Bounded Convergence Framework (BCF), a three-zone governance model organizing AI deployment into an inner zone of irreducibly human clinical functions, a practitioner-governed boundary zone defined by three non-negotiable threshold conditions, and an outer zone of legitimate AI-assisted functions. Sixteen governance principles were identified, with particular implications for contraindication screening, crisis escalation architecture, AI identity disclosure, and the protection of adolescent and trauma-exposed populations. The BCF offers the first empirically grounded governance framework for AI deployment in mindfulness-based mental health delivery, with direct implications for platform developers, health service administrators, regulators, and clinical practitioners navigating this rapidly expanding and underregulated domain.
Comparable studies
Other qualitative studies on meditation, most cited first.
Citations in the library
Cites 8
- Mechanisms of mindfulness Journal of Clinical Psychology December 29, 2005
- How Does Mindfulness Meditation Work? Proposing Mechanisms of Action From a Conceptual and Neural Perspective Perspectives on Psychological Science October 14, 2011
- The varieties of contemplative experience: A mixed-methods study of meditation-related challenges in Western Buddhists PLoS One May 24, 2017
- Chatbot-Based Mindfulness-Based Stress Reduction Program for University Students With Depressive Symptoms: Intervention Development and Pilot Evaluation. Journal of the American Psychiatric Nurses Association 2025
- The Teacher Matters: The Role and Impact of Meditation Teachers in the Trajectories of Western Buddhist Meditators Experiencing Meditation-Related Challenges Contemporary Buddhism April 22, 2025
and 3 more in the library