The externalization of internal experiences in psychotherapy through generative artificial intelligence: a theoretical, clinical, and ethical analysis.

INTRODUCTION

Externalization techniques are well established in psychotherapy approaches, including narrative therapy and cognitive behavioral therapy. These methods elicit internal experiences such as emotions and make them tangible through external representations. Recent advances in generative artificial intelligence (GenAI), specifically large language models (LLMs), present new possibilities for therapeutic interventions; however, their integration into core psychotherapy practices remains largely unexplored. This study aimed to examine the clinical, ethical, and theoretical implications of integrating GenAI into the therapeutic space through a proof-of-concept (POC) of AI-driven externalization techniques, while emphasizing the essential role of the human therapist.

METHODS

To this end, we developed two customized GPTs agents: VIVI (visual externalization), which uses DALL-E 3 to create images reflecting patients' internal experiences (e.g., depression or hope), and DIVI (dialogic role-play-based externalization), which simulates conversations with aspects of patients' internal content. These tools were implemented and evaluated through a clinical case study under professional psychological guidance.

RESULTS

The integration of VIVI and DIVI demonstrated that GenAI can serve as an "artificial third", creating a Winnicottian playful space that enhances, rather than supplants, the dyadic therapist-patient relationship. The tools successfully externalized complex internal dynamics, offering new therapeutic avenues, while also revealing challenges such as empathic failures and cultural biases.

DISCUSSION

These findings highlight both the promise and the ethical complexities of AI-enhanced therapy, including concerns about data security, representation accuracy, and the balance of clinical authority. To address these challenges, we propose the SAFE-AI protocol, offering clinicians structured guidelines for responsible AI integration in therapy. Future research should systematically evaluate the generalizability, efficacy, and ethical implications of these tools across diverse populations and therapeutic contexts.