Automated systems for coding psychotherapeutic discourse

Context and relevance. Currently, the development of machine learning and natural language processing technologies has made significant progress in terms of analyzing the intentional, psychological structure of texts. It is important to assess the capabilities and limitations of these technologies in solving such problems. We consider the achievements in this scientific field using the example of automated coding systems for psychotherapeutic discourse. Objective. To analyze modern international articles concerning the development and application of automated coding systems for psychotherapeutic discourse, to identify the main approaches to coding automation, and to characterize the range of tasks solved using machine learning technologies Hypothesis. Automated coding systems for psychotherapeutic discourse are used to solve a wide range of research and applied tasks. Methods and materials. Systems were used to search for articles arxiv.org, APA PsycNet, frontiers, ResearchGate, ACL Anthology, Taylor & Francis online, Nature, Semantic Scholar, Science Direct, Wiley Online Library. The search was carried out by keywords: «psychotherapy», «discourse», «conversationalism», «behavioral coding», «deep learning», «large language models». The main focus was on publications for 2020/23-2025. Results. Currently, three main approaches are being implemented to automate coding systems for psychotherapeutic discourse: traditional (with expert selection of features for discourse categories), LLM-prompting (using large language models through special instructions —promptov), finetuning (further training models on specialized data). Automated coding systems are used to solve a wide range of tasks: studying the structure and dynamics of the psychotherapeutic process, assessing qualifications, training and supporting psychotherapists, analyzing the impact of discourse features on the therapeutic alliance and the outcome of therapy. Conclusions. The analysis of publications has shown that modern artificial intelligence technologies are capable of encoding psychotherapeutic discourse at a level comparable to humans. This significantly expands the possibilities of using automated systems in solving research and applied (training and support of psychotherapists) tasks.

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