Introduction: Rethinking the Future of Mental Wellbeing in an AI World
The future of mental wellbeing is no longer shaped only in clinics or therapy rooms. It is increasingly influenced by everyday technologies that observe, respond, and sometimes even simulate empathy. Artificial intelligence now sits quietly in the background of apps that track mood, chat with users, or suggest coping strategies when stress levels rise.
Yet mental wellbeing is not just about symptom reduction. From a humanistic perspective, it is about feeling alive, connected, and capable of growing into oneself. The PERMA framework developed by Martin Seligman offers a useful structure for understanding this fuller picture: Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment.
When AI is examined through this lens, it becomes clear that its role is not simply technical. It is deeply human. The question is not whether AI can support mental health, but whether it can do so in a way that preserves dignity, depth, and emotional authenticity.
Positive Emotion: Can AI Genuinely Support How We Feel?
Positive emotion is often the first area where AI enters mental health conversations. Many people now turn to digital companions for comfort during moments of anxiety, sadness, or overwhelm. These systems use language patterns and behavioural cues to offer calming responses, breathing guidance, or gentle reframing of thoughts.
Research suggests that AI-supported interventions can reduce short-term distress, particularly in mild anxiety and stress-related conditions (Fitzpatrick et al., 2017). However, the humanistic concern is not only whether mood improves, but how it improves.
Fictional case study – 01
Amira, a 32-year-old nurse, uses an AI wellbeing assistant after emotionally exhausting shifts. When she types that she feels drained, the system responds with reflective prompts like “That sounds heavy, would you like to slow your breathing together for a moment?”
She finds it helpful in the moment, but she also notices something subtle. While the AI is consistent and available, it does not truly understand her world of responsibility, grief, and care fatigue. The comfort is real, but partial.
This illustrates a key truth for the future of mental wellbeing. AI can support emotional regulation, but it cannot fully hold human experience in its complexity.
Engagement: Rebuilding Attention in Fragmented Lives
Engagement in PERMA refers to being deeply absorbed in meaningful activity. Yet modern attention is often fragmented, pulled in multiple directions by digital noise. AI systems attempt to respond to this by guiding users toward structured routines, mindfulness practices, or personalised focus sessions.
Digital mental health tools using adaptive algorithms have shown improved engagement in self-guided therapeutic exercises (Inkster et al., 2018). The strength of AI lies in its ability to personalise timing and intensity based on user behaviour.
Fictional case study 02
Daniel, a postgraduate student, uses an AI wellbeing app during exam preparation. When his sleep becomes irregular, the system gently adjusts his daily plan, reducing cognitive load and introducing short grounding exercises.
Over time, he notices something interesting. It is not just that he is more productive. It is that he feels less scattered within himself. The AI does not create focus, but it helps him return to it.
Still, engagement shaped by algorithms raises a deeper question. If attention is always guided from outside, do we lose something of our inner compass?
Relationships: The most Sensitive Frontier of AI Wellbeing
Relationships are at the heart of human flourishing. They are also the most delicate dimension of AI involvement in mental health.
AI companions can simulate conversation, remember preferences, and respond in emotionally attuned ways. For people experiencing loneliness, this can feel like relief. Studies suggest that conversational agents can reduce perceived loneliness in the short term (Bickmore et al., 2018).
However, humanistic psychology reminds us that relationships are not only about response. They are about mutual presence, unpredictability, and shared emotional risk.
Fictional case study – 03
Eleanor, a 74-year-old retired teacher, uses an AI companion daily. It greets her, asks about her memories, and encourages her to contact friends. She feels less alone.
Yet over time, she begins to rely more on the AI conversation than on local community groups. The interaction feels easier, more predictable, and less emotionally demanding.
This reveals a quiet tension in the future of mental wellbeing. AI can soften loneliness, but it can also unintentionally replace the friction and richness of real human connection.
Meaning: The Deeply Human Search that AI can only Support
Meaning is not something that can be programmed. It emerges through lived experience, suffering, joy, and reflection. AI systems now attempt to support meaning-making through journaling prompts, value clarification exercises, and narrative analysis.
In digital mental health research, reflective AI tools have been shown to support self-awareness and insight generation (Mohr et al., 2017). Yet meaning cannot be delivered. It can only be uncovered.
Fictional case study – 04
Arjun, a 41-year-old professional, begins using an AI wellbeing system during a period of burnout. The tool asks him reflective questions about moments when he felt most alive and engaged.
Gradually, he realises that his strongest sense of fulfilment came not from corporate achievements, but from mentoring junior colleagues. This insight leads him to shift his career path toward teaching.
The AI did not give him meaning. It simply helped him hear his own voice more clearly.
Accomplishment: Progress, Motivation, and Gentle Structure
Accomplishment in PERMA is about progress and mastery, not external validation alone. AI systems are particularly strong here because they can track habits, provide feedback, and adjust goals dynamically.
Behavioural activation supported by digital tools has shown positive outcomes in depression management (Ekers et al., 2014). Small, structured goals can help rebuild momentum when motivation is low.
Fictional case study – 05
Liam, a young adult experiencing depressive symptoms, uses an AI-guided wellbeing programme. Each day, it suggests small achievable tasks such as walking outside or completing one household activity.
At first, the steps feel insignificant, but over weeks, they accumulate into something meaningful. He begins to experience a return of agency.
Still, there is a limitation. Accomplishment becomes most powerful when it is personally chosen, not only system-suggested.
The Humanistic Challenge: What Must Remain Human?
A purely technological view of mental wellbeing risks reducing people to data patterns. A humanistic view insists that people are not systems to be optimised, but lives to be understood.
AI can support emotional regulation, structure behaviour, and increase access to care. But it cannot fully replace empathy grounded in shared humanity.
The future of mental wellbeing depends on balance. Not replacement, but relationship between human and machine intelligence.
Conclusion: A Future Shaped by Support, not Substitution
AI will continue to shape mental wellbeing in powerful ways. It will offer tools for reflection, structure, and emotional support that are more accessible than ever before.
Yet the PERMA framework reminds us that wellbeing is not only about function. It is about flourishing in the fullest sense of being human.
The most hopeful future is not one where AI becomes the caregiver, but one where it becomes a quiet companion in a larger human ecosystem of care, meaning, and connection.
References
- Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behaviour therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785
- Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR mHealth and uHealth, 6(11), e12106. https://doi.org/10.2196/12106
- Bickmore, T. W., Gruber, A., & Picard, R. W. (2018). Establishing the computer–patient working alliance in automated health behavior change interventions. Patient Education and Counseling, 101(4), 652–658. https://doi.org/10.1016/j.pec.2018.06.003
- Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013). Behavioral intervention technologies: Evidence review and recommendations for future research in mental health. General Hospital Psychiatry, 35(4), 332–338. https://doi.org/10.1016/j.genhosppsych.2013.03.008
- Ekers, D., Webster, L., Van Straten, A., Cuijpers, P., Richards, D., & Gilbody, S. (2014). Behavioural activation for depression: An update of meta-analysis of effectiveness and subgroup analysis. PLoS ONE, 9(6), e100100. https://doi.org/10.1371/journal.pone.0090810
- Seligman, M. E. P. (2011). Flourish: A visionary new understanding of happiness and well-being. Free Press.