Generative Artificial Intelligence in post-session clinical reflection. The risk of gentle collusion

The rapidly increasing use of generative Artificial Intelligence (AI) in psychotherapy has been discussed mainly in terms of patient-facing chatbots and the possible replacement of clinicians. Focusing on an increasingly common practice, the therapists’ use of large language models (LLM) after the sessions to summarize, seek alternative readings, or obtain reassurance is examined. Drawing on a one-year self-experimentation, a specific risk termed “gentle collusion” is described: a premature closure in which the fluency and validating tone of AI outputs are mistaken for an understanding that has been clinically worked through. The argument is framed within a fallibilist epistemology of clinical work, where uncertainty, negative capability, and the temporality of elaboration are conditions for thinking. After revisiting the ELIZA effect and recent evidence on sycophancy and the simulation of evaluative judgement, the paper clarifies why AI assistance is not equivalent to human supervision and how it can erode source monitoring and perceived authorship. The final section proposes minimal safeguards for responsible use: preserving a short delay between session and consultation, starting from descriptions closest to the session rather than ready-made interpretations, formulating disconfirming questions, reintegrating AI outputs into supervision and peer discussion, and applying strict de-identification and data minimization. The main clinical issue is not factual accuracy, but the way meaning becomes thinkable and owned by the therapist.

Keywords: Artificial intelligence (AI); Gentle collusion; Clinical reflexivity; ELIZA effect; Psychotherapy

Sabrina Signorini, L’uso dell’intelligenza artificiale nella riflessione clinica. Il rischio della collusione gentile in "PSICOTERAPIA E SCIENZE UMANE" 2/2026, pp 239-256, DOI: 10.3280/PU2026-002003