AI & Mental Health Research

People now tell AI systems even their most private thoughts.

I study the conditions under which that relationship can be supportive, and the risks it can carry for mental health. My aim is to help make the use of AI in mental health care safe, transparent, and accountable.

ROLE
Clinical psychologist, mental health expert, AI researcher
FOCUS
Safe use of generative AI in mental health care
AFFIL
EFPA Project 13 Expert Working Group · PSI Graduate Member
NOW
Üretken Yapay Zekâ ve Ruh Sağlığı (Nobel, 2026)
GROWTH
IBM Generative AI Engineering Specialization (Coursera, in progress)

Why

Why does a mental health expert study AI?

Because where people take shelter has changed.

People used to entrust their pain to a friend, a notebook, a line of prayer, a therapist, sometimes to the silence of the night that answers to no one. Today some of those lines are typed into a screen. People now open their loneliness, their shame, the knots they cannot untie not only to another person but to a screen that answers back.

That is why, for me, AI is not only a technical advance. It is the mood of an era. One of the new mirrors in which we look at ourselves.

In a counseling room in Istanbul I have watched for years how anxiety tightens in the body and how loneliness thins a person's voice. The search for belonging can carry someone even out of their own story. The same sentences are now typed into a chatbot or a personalized AI tool, in Komotini, in Istanbul, in Dublin.

People have not changed. Only the place where they tell their troubles has.

So the relationship between mental health and AI cannot be understood in the language of model capacity, efficiency, or technological progress. Any system that touches a person where they are fragile, whether in the therapy room or behind a screen, carries a psychological responsibility. Because the question is not what is possible; the question is what the possible does to a person.

My work stands exactly on this threshold. How can generative AI be used in mental health safely and accountably, with human dignity kept in view? As a system comes to know someone better, does it truly draw closer to them, or only touch the weak spot more skillfully? When does personalization become support, and when does it turn into a pull that feeds a cycle of attachment and surrender?

My concern is not to put AI in the place of therapy or of human relationships. Such a claim would misread both the person and the technology. My concern begins somewhere more basic.

We cannot assume that a technology which touches the human mind is innocent.

We cannot settle for a system that enters a person's privacy merely because it works.

We cannot exempt a tool that answers human pain from clinical responsibility.

This is why I study AI. Because the work of a mental health expert does not end with waiting in the therapy room. Wherever people carry their troubles, whatever new door they leave their hope at, clinical attention has to go there too. Someone typing into a screen in Komotini deserves that same attention, even if they never walk into the room.

What I do is build a language of safety between psychology's long understanding of the person and the new obligations of the age of AI. That language has to carry clinical responsibility into the engineering decision itself.

Because the future will also be built by those who ask, in time, what these systems do to the human spirit.

What happens when a person is left alone with the answer? Asking that in time is the name of the work I do.

Research spine

Three research lines.

Before technical novelty, I look at human vulnerability. What a system should not touch is also a design question.

01

Clinical safety

Crisis, privacy, referral, false reassurance, and harm reduction. In mental health contexts, I study when a system should stop, when it should slow down, and when it should hand a person over to a professional.

02

Algorithmic co-regulation

How human and model soothe, steer, strengthen, or make each other dependent. ACoR reads this not as tool use, but as a process of co-regulation unfolding between two parties.

03

Attachment and reinforcement

Loneliness, personalization, emotional bonding, use loops, and loss of boundaries. HPR models when a relationship with an AI system stays supportive, when it tips into reinforcement, and when it opens onto addiction-like patterns.

Research lines

The questions I am working on.

A selection of publications and work in progress where mental health meets AI. I state each status as it currently stands.

  1. SubmittedAlgorithmic co-regulation: A formal human-computer interaction theory of calibrated reliance, self-regulation, and autonomy in adaptive AI systemsInteracting with Computers
  2. SubmittedThe Hyper-Personalized Reinforcement Model: How Generative AI May Transform Behavioural Addiction MechanismsPsychiatry Research Communications
  3. Under peer reviewEmotional Attachment and Problematic Involvement with Socially Interactive AI: A Critical Systematised Narrative Review of Embodied and Conversational SystemsAddictive Behaviors Reports
  4. AcceptedWhen Machines Become Attachment Figures: Developing an AI Attachment and Dependency ScaleICBA 2026, International Congress on Behavioral Addictions
  5. SubmittedAttitudes, learning intentions, and ethical evaluations of artificial intelligence among mental health professionals: a PRISMA 2020 systematic reviewJMIR Mental Health
  6. SubmittedSwarm Intelligence Meets Social and Political Psychology: LLM-Based Multi-Agent Simulation as a Computational Social LaboratoryMethods in Psychology
  7. PublishedFeasibility of animal-assisted therapy in the treatment of depressionCurrent Approaches in Psychiatry, 16(3), 2024
  8. Under peer reviewAn Examination of the Experiences of Being a Minority Among Young Adults of Western Thrace Turks and Istanbul GreeksSoutheast European and Black Sea Studies

Books

Two books, two layers.

One brings generative AI into mental health through clinical safety and ethical limits. The other studies human strengths, the search for meaning, and well-being.

Open source

Three nodes, one intent.

Memory, verification, and social science practice. Three open-source projects working to make AI accountable, connected as a single network that carries signals between them.

mnemePersistent memory
mergenVerification layer
claude-code-for-social-scientistsToolkit for social scientists
Open source · Apache-2.0 license

mneme

Most AI agents reset their memory every session. mneme closes that gap: a persistent memory system that stores context, recalls it across sessions, and shares it across multiple clients. I wrote it so a researcher's work does not start over with every session.

Explore the details onourimpram.github.io/mneme
Open source · Apache-2.0 license

mergen

AI agents write more and more code, yet it remains hard to know whether what they produce has actually been verified. mergen is a verification and governance layer that aims to make agent output accountable. It asks for the evidence behind a change, sets verification gates, and grounds trust in proof rather than claim.

Explore the details github.com/OnourImpram/mergen
Open source · Zenodo DOI

claude-code-for-social-scientists

Social scientists now hold powerful AI tools, yet the path to using them within clinical and ethical bounds is rarely written down. I wrote this guide and toolkit for social scientists who want to work with AI while protecting data security and research integrity.

Explore the details github.com/OnourImpramZenodo DOI My GitHub

Models improve, tools are replaced; responsibility cannot be handed off to a model. A person always carries it.

My work is to hold that line: to make sure that when a system reaches a person's most fragile place, clinical attention is still standing there.

Contact

For a question, an invitation, or a collaboration, write to [email protected] or reach me on WhatsApp.

The clinical side

Behind all of this is a therapy room.

The research does not start from an abstract place. It grows out of the practice of a clinical psychologist who meets people's stories every day. The core principle I learned in the therapy room has not changed. Without a safe relationship, no deep change can be expected.

That is why my AI work, too, puts the human rather than the technology at its centre. Mental health on one side, AI research on the other. And in the middle, still, the human.