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PERSPECTIVE article

Front. Psychiatry

Sec. Digital Mental Health

Artificial Intelligence-based Psychotherapy: Focusing on Common Psychotherapeutic Factors

Provisionally accepted
  • Independent researcher, Athens, Greece

The final, formatted version of the article will be published soon.

Artificial intelligence-based psychotherapy applications have been evolving rapidly in recent years. They seem to offer solutions to a complex world with enormous mental health needs. Easy access, immediacy and low cost are their enduring advantages, while anonymity attracts people who are isolated by the stigma of mental illness. Artificial intelligence-based psychotherapy applications borrow and incorporate elements of the already proven common psychotherapeutic factors, as described in the so-called 'contextual model'. As decades of practice have shown, these 'common factors' seem to prevail in every type of in-person psychotherapy. They are the key elements of their successful outcome and the main reason for the lack of superiority of one type of psychotherapy over another. A key area here is therapeutic alliance, characterized by the therapist's empathy, the patient's expectations, and the shared therapeutic goals. Could artificial intelligence design opportunities so that these factors become even more useful in AI psychotherapy? Improvement in the development of an empathetic therapeutic relationship environment, based on the 'theory of common factors', are expected to facilitate the adaptation of interventions and further increase the reliability and effectiveness of AI psychotherapy.

Keywords: Psychotherapy, AI Psychotherapy, common factors, Τherapeutic alliance, eCBT, Artificial Intelligence- based Psychotherapy

Received: 22 Sep 2025; Accepted: 28 Oct 2025.

Copyright: © 2025 Giotakos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Orestis Giotakos, orestis@obrela.gr

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