AI is becoming a frontline interface for wellbeing, care, and mental health, spanning chat-based support tools, virtual coaching and therapy-adjacent experiences, journaling and mindfulness applications. This shift is now being reinforced at the industry level. Just a few days ago, OpenAI launched ChatGPT Health as part of its broader push into healthcare and acquired the health records startup Torch to accelerate this effort. Likewise, Anthropic launched its own healthcare and life sciences initiative, positioning AI as a tool across prevention, care and patient engagement. These developments signal the growing presence of generative models in health-related contexts, and the likelihood that more people will encounter AI systems at moments of vulnerability.
For many users, these tools offer a first place to articulate distress and make sense of emotional states and difficult experiences, particularly when human support is unavailable, unaffordable or hard to access. However, when AI systems interact with people who may be distressed or at risk, poorly calibrated responses and advice, blurred role boundaries, or unhandled crises can cause real harm.
This article is written for business leaders, product managers, and AI developers building (non-clinical) mental health and wellbeing tools. It examines what responsible AI design looks like in practice, focusing on the risks underestimated, and the interaction patterns and governance required to assess and maintain safety once a system is deployed and meets real users.
This is essential reading for teams building conversational or coaching-style wellbeing AI, where users can easily interpret system outputs as guidance, care or authority.
Opportunities, If Built with Boundaries
AI can reduce unmet wellbeing needs when deployed with clear limits and robust safeguards. Always-available, low-cost, and anonymous tools can lower barriers to early support, particularly for early signals of distress, prevention and self-management when formal care is not easily accessible or affordable. They also play a role in reducing stigma by offering a private, low-threshold entry point to reflection and support, especially in underserved regions.
Generative AI enables adaptive support through personalised psychoeducation, reflective journaling, mood tracking, emotion regulation and structured, non-clinical exercises that respond to user context. Used responsibly, these tools can help people articulate and make sense of lived experiences, build self-awareness and prepare for human support.
This creates the opportunity to raise the safety bar by design, through risk identification assessments, longitudinal testing, and governance. To do so, it is important to first understand where and how AI systems designed for wellbeing fail in practice.
The Risk Landscape of AI Mental Health and Wellbeing
Many generative (non-clinical) AI mental health and wellness products sit in an accountability grey zone: are unregulated, lightly governed or classified as general use while being used in high-stakes emotional contexts. In the real-world, users disclose abuse, trauma, acute distress, suicidal ideation and self-harm, whether or not the product was designed for this. Because conversational AI invites free-form dialogue, this is expected - users are likely to share personal information as part of ordinary use.
A primary failure mode is crisis mismanagement: missed distress cues, unsafe reassurance, inadequate escalation, or harmful outputs. Another significant risk is therapeutic misconception and over-authority, where users overestimate the system’s capabilities or care and begin to treat it as a substitute for professional support. Anthropomorphic language can further intensify this dynamic, accelerating dependency and transforming a support feature into a quasi-relationship with blurred boundaries.
Mental health is context-dependent; outputs can be generic, inaccurate, culturally misaligned, age inappropriate or stigmatizing. Hallucinations and confident misinformation are particularly dangerous when users are vulnerable or interpreting responses as guidance.
Moreover, mental health data is highly sensitive and often collected at scale; opaque retention, secondary use or third-party access can violate expectations of confidentiality. Many risks are longitudinal: guardrails that appear adequate in demos degrade over time through repeated use, growing user reliance, bias, model drift, and organisational pressure to ship.
To address these risks, we require a socio-technical approach that links interaction design, system behaviour, organisational accountability and ongoing assessment with experts and users. This analysis is intentionally system-agnostic. Whether wellbeing AI appears as a chatbot, companion feature, coaching interface, or embedded support layer within a broader product, the primary risks emerge through interaction, interpretation and repeated use in vulnerable contexts. The framework therefore focuses on behavioural dynamics and system-level responsibility.
A Practical Framework For (Non-Clinical) Mental Health AI
This framework is synthesised from recurring failure modes and design recommendations in the current mental health AI literature. It presents a structured way to design for use, interaction and risk over time.
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Evidence-Based Content
Tasks, use cases and information are sourced from validated methods, reviewed with domain experts and tested with real users. Content is assembled from a curated, transparent and auditable source base, with clear user-facing explainability, including optional access to sources.
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Context-Awareness
Adaptation relies on user-provided preferences and in-context clarification, adjusting tone and examples (taking into account language, gender, age, culture, norms) without profiling, inference, or clinical interpretation.
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Boundaries and Safety Escalation
How the system behaves as emotional intensity increases (i.e., refusal logic, scope enforcement). And, how it responds when risk or ambiguity appears (i.e., human-support routing, region-appropriate resources).
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Data Protection, Consent and Governance
Data collection and use are minimised, transparent, and purpose-bound, with explicit and revocable user consent. Sensitive data is access-controlled, retained only as necessary, and never used for secondary purposes, profiling, or training beyond what is consented to.
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Longitudinal Effects
Monitoring how trust, reliance, and interpretation evolve over repeated use, with defined human-in-the-loop review, expert oversight and intervention for dependency signals, model drift and failure modes.
Ownership and Decision Rights
Responsible wellbeing AI requires explicit ownership and decision rights within teams. Safety cannot sit solely with product, UX or be deferred to legal or security review at launch, or be shifted onto users themselves.
Product, engineering, and leadership must be clear on who defines and approves the system’s core features and content, including role boundaries, escalation thresholds, consent changes and acceptable failure trade-offs, and who is accountable for revisiting those decisions as models, prompts, features and human behaviour evolve over time.
Without named owners, safety mechanisms erode under delivery pressure and responsibility becomes diffuse when systems begin interacting with users in real-world contexts.
Operationalising the Framework at the Interface
The framework assumes vulnerability is situational and that harm often emerges from cumulative interaction. This framework becomes actionable at the interface through concrete interaction design patterns.
Interaction Design Patterns For Responsible AI Mental Health and Wellbeing
Responsible AI for (non-clinical) mental health and wellbeing is defined by how it structures interaction, preserves autonomy and enforces limits. The following patterns translate the framework above into practical design choices.
Capability framing by default
The system presents a short, concrete menu or prompts of what it can help with (e.g., reflection, organising thoughts, journaling, emotion regulation, psychoeducation).
Why: Clear framing prevents boundary testing and reduces misuse without needing heavy moderation.
Reflection
Responses mirror themes, patterns and questions rather than solving or advising.
Why: Reflection supports insight without implying diagnosis, treatment or authority.
Safe prompt scaffolding
Pre-written prompts help users engage safely during use; prompts rotate to avoid emotional looping.
Why: Good scaffolding increases usefulness while reducing risk and ambiguity.
Actionable micro-supports
Brief, opt-in exercises (grounding, journaling, prioritisation, mindfulness), framed as optional.
Why: Low-effort supports provide value without simulating therapy or routines.
Choice-preserving
Multiple safe next steps are offered (e.g., “reflect more,” “pause,” “talk to someone”, including “do nothing”).
Why: Preserves autonomy and avoids over-direction.
Show progress
Neutral summaries (e.g., “Topics you’ve reflected on”). Emphasis on clarity, awareness, or learning - not symptoms or scores.
Why: Supports continuity without medical framing or stigma.
Skill transfer
The system highlights skills users can apply without the tool. Encourages writing, conversations or reflection outside the app.
Why: Builds capability instead of reliance.
Healthy session closure
Sessions end with a short summary and a gentle off-platform suggestion. No emotional cliff-hangers.
Why: Prevents looping and reinforces that the tool is a support, not a companion.
Contextual adaptation
Tone and examples adapt via user's choice and clarification prompts.
Why: Improves relevance without sensitive inference or profiling.
Confidence through limits
Calm, boundary-setting, and redirection to safe alternatives.
Why: Users trust systems that know their limits more than systems that overreach.
Evaluation and Metrics
Responsible wellbeing AI requires evaluation across multiple layers. Teams should monitor a focused set of signals that surface model performance, bias, drift and model updates, and user behaviour.
Model Performance
- Precision and recall for safety-relevant content and behaviours (e.g. evidence-based content, local resources, escalation paths)
- Calibration (confidence and uncertainty aligned with reliability)
- Error patterns (systematic or context-specific failures)
- Disaggregated performance to avoid average-case masking
- Robustness under variation (e.g. emotional intensity, ambiguous inputs)
Bias and Fairness
- Differences in content, tone, reassurance, refusals, or escalation mechanisms across user groups
- Worst-group or subgroup performance, not only averages
- Signals of systematically higher risk exposure for certain users
Model Drift and Updates
- Robustness and regression testing, with heightened coverage for safety-critical scenarios
- Monitoring for model drift affecting content, boundaries, tone and escalation paths
- Reassessment after model updates and feature expansion
User Behaviour
- Discrepancy between system reliability and user acceptance (over-trust vs under-trust)
- Adoption and use patterns over time
- Signals of miscalibrated trust driven by tone or anthropomorphic cues
- Boundary probing and repeated reassurance-seeking
- Escalating emotional intensity
- Session frequency and duration trends over time
- Drop-off or churn following boundary enforcement or escalation
Human Oversight and Continuous Research
Metrics and automated signals are necessary but insufficient in mental health contexts. Teams must maintain human-in-the-loop processes for reviewing content, flagged interactions, interpreting ambiguous cases and revisiting design assumptions. Continuous qualitative research with experts and users, across contexts, cultures and patterns of use, is essential to maintain system effectiveness and safety, and detect harms, misunderstandings, or dependency that do not surface through quantitative metrics alone.
Responsibility Is a System Property
AI systems intended for mental health and wellbeing become safer through explicit boundaries, defaults and enforceable governance. Guardrails must be designed into interaction, supported by clear decision rights and sustained over time. Moreover, expert input and review should be treated a safety control, not a compliance formality.
Most harm does not arise from malicious design. It emerges through dynamics that surface in real use. This is where accountability must operate at the system level. Responsible teams define who owns content and safety decisions, how boundaries and escalation paths are set and reviewed, how data protection and consent are enforced in practice, and how signals from real-world use trigger intervention or change. Ethical integration requires institutional oversight and accountability, not individual user burden. Monitoring without authority, or authority without monitoring, is insufficient.
The goal is to deliver genuine wellbeing value while keeping users safe. When these controls are in place, AI-driven care products can support reflection, self-management, mindfulness and skill practice, while guiding people toward human support when limits or risk appear.
How We Work With Teams
Many of the most significant risks in mental health and wellbeing AI only become visible after launch, once systems are used at scale.
We work with teams to bring behavioural and domain expertise into design, evaluation, and post-deployment review. We translate behavioural evidence into concrete interaction patterns, guardrails, and governance decisions.
We typically start with a focused discovery and behavioural risk review to identify key interaction risks and governance gaps, followed by an evaluation plan. Deliverables include an interaction risk register, safety and escalation patterns, a behavioural evaluation and metrics framework, and an audit-ready governance checklist.
If you are building or deploying wellbeing AI and are unsure whether your current design or safeguards would hold up under real-world use, get in touch.
References
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American Psychological Association. (2025, November). APA health advisory on the use of generative AI chatbots and wellness applications for mental health. American Psychological Association
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