PERSPECTIVE article
Front. Psychiatry
Sec. Digital Mental Health
Artificial Intelligence-based Psychotherapy: Focusing on Common Psychotherapeutic Factors
Provisionally accepted- Independent researcher, Athens, Greece
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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
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.