Relationship-Centered Care: Relatedness and Responsible Design for Human Connections in Mental-Health Care

2026-03-18

AI Summary

Current AI-powered psychotherapeutic agents often overemphasize simulating clinician–patient rapport, thereby creating an illusion of connection that may undermine patients’ authentic interpersonal relationships and impede long-term recovery. This work proposes a paradigm shift in design focus—from mimicking therapeutic relationships to actively supporting genuine human connections. Integrating the “relatedness” component of Self-Determination Theory with the six-dimensional framework of Responsible AI, the study introduces a relationally centered design paradigm for mental health AI systems. Grounded in this synthesis, the proposed approach aims to strengthen patients’ broader relational ecosystems through concrete design principles, heuristic questions, and system-level guidelines, ultimately fostering a sustainable, human-centered ecosystem for AI-mediated psychological interventions.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

There has been a growing research interest in Digital Therapeutic Alliance (DTA) as the field of AI-powered conversational agents are being deployed in mental health care, particularly those delivering CBT (Cognitive Behaviour Therapy). Our proposition argues that the current design paradigm which seeks to optimize the bond between a patient in need of support and an AI agent contains a subtle but consequential trap: it risks producing an "appearance of connection" that unintentionally disrupts the fundamental human need for relatedness, which potentially displaces the authentic human relationships upon which long-term psychological recovery depends. We propose a reorientation from designing artificial intelligence tools that simulate relationships to designing AI that scaffolds them. To operationalize our argument, we propose an interdisciplinary model that translates the Responsible AI Six Sphere Framework through the lens of Self-Determination Theory (SDT), with a specific focus on the basic psychological need for relatedness. The resulting model offers the technical and often clinical communities a set of relationship-centered design guidelines and relevant provocations for building AI systems that function not just as companions, but as a catalyst for strengthening a patient's entire relational ecology; their connections with therapists, caregivers, family, and peers. In doing so, we discuss a model towards a more sustainable ecosystem of relationship-centered AI in mental health care.

Problem

• Digital Therapeutic Alliance
• Relatedness
• Responsible AI
• Relationship-Centered Care
• Mental Health Care

Innovation

• Relationship-Centered Design
• Digital Therapeutic Alliance
• Responsible AI
• Self-Determination Theory
• Relatedness