AI-Assisted Precision Psychotherapy: Toward a Process-Based Framework of Therapeutic Interaction

Abstract

Psychotherapy is an effective treatment for a wide range of mental disorders, yet treatment outcomes remain highly variable across patients. The emerging field of precision psychotherapy seeks to address this variability by tailoring interventions to the characteristics and needs of individual patients. Recent advances in artificial intelligence (AI) have accelerated this effort by enabling predictive models that identify patients at risk for treatment non-response and support personalized treatment selection. However, most current approaches to precision psychotherapy focus primarily on predicting outcomes or stratifying patients into subgroups, while offering limited tools for supporting clinical decision-making within the therapeutic process itself. This paper proposes that advancing precision psychotherapy may benefit from conceptual frameworks capable of representing therapeutic interactions as they unfold within clinical sessions. Psychotherapy is fundamentally a communicative process in which psychological change is understood to emerge through the evolving dialogue between therapist and patient. To help structure this process, we introduce a dimensional framework that conceptualizes mental activity as organized across five hierarchical dimensions: action, thought, emotion, experience, and being. These dimensions provide a structured representation of the forms in which psychological activity appears in therapeutic dialogue and may allow therapeutic interaction to be described as a sequence of dimensional transitions between therapist and patient. Such a representation could serve as a conceptual basis for the future development of computational tools aimed at analyzing therapeutic dialogue and identifying patterns that may be associated with therapeutic processes such as change, rupture, or shifts in interaction. Integrated with AI-based analysis of psychotherapy sessions, this framework may inform the design of systems intended to support the analysis of therapeutic processes, such as monitoring patterns of interaction or identifying potential shifts in patient experience, while preserving the central role of clinical judgment. By providing a formalized representation of therapeutic dialogue, this approach is intended as a conceptual and methodological step toward the development of more precise, process-informed approaches to psychotherapy.

1. Introduction

1.1. Variability in Psychotherapy Outcomes

Psychotherapy is widely recognized as an effective treatment for a broad range of mental disorders, including depression, anxiety disorders, trauma-related conditions, and personality disorders [1]. Across diverse therapeutic modalities such as cognitive-behavioral therapy, psychodynamic therapy, and interpersonal therapy, randomized controlled trials and meta-analyses consistently demonstrate meaningful improvements in symptoms, functioning, and quality of life for many patients [2].

However, despite this robust evidence base, psychotherapy research continues to face a central challenge: understanding why treatments that are effective on average produce highly variable outcomes across individual patients. A substantial proportion of patients do not achieve clinically significant improvement, some discontinue treatment prematurely, and others relapse following initial gains [1]. This variability reflects the marked heterogeneity of mental disorders and of the individuals who experience them. Patients differ in symptom profiles, developmental histories, interpersonal patterns, psychological capacities, and neurobiological characteristics [3]. In addition, therapeutic processes themselves vary across therapists, treatment settings, and relational dynamics within the therapeutic encounter [4]. As a result, treatments that are effective on average may not be equally effective for all patients. Understanding and addressing this variability has therefore become a central challenge in contemporary psychotherapy research [5].

1.2. Emergence of Precision Medicine, Precision Psychiatry, and Precision Psychotherapy

In response to similar challenges across medical disciplines, the concept of precision medicine has emerged over the past two decades as a paradigm for tailoring prevention, diagnosis, and treatment to the characteristics of individual patients. Rather than relying solely on average treatment effects derived from large populations, precision medicine integrates multiple sources of information (including genetic, biological, behavioral, and environmental data) to guide clinical decision-making for specific individuals [6,7].

Psychiatry has increasingly adopted similar aspirations through the development of precision psychiatry. This emerging field integrates advances in neuroimaging, computational modeling, digital phenotyping, and large-scale data analytics in order to better characterize psychiatric disorders and predict treatment response at the level of the individual patient [8,9,10,11]. These approaches aim to detect patterns within complex datasets that may inform more individualized diagnostic and therapeutic strategies.

Within this broader movement, the concept of precision psychotherapy has begun to emerge. While pharmacological research has been a central focus of precision psychiatry, psychotherapy represents an equally important component of psychiatric treatment. In recent years, researchers have begun to explore how principles of precision medicine might be applied to psychotherapeutic care. Emerging formulations, such as data-informed psychological therapy or precision psychological therapy, emphasize the integration of empirical data into clinical decision-making in psychotherapy [12].

These developments reflect a growing recognition that psychotherapy outcomes vary substantially across patients and that greater precision in treatment planning and therapeutic intervention may improve clinical effectiveness. At the same time, efforts to conceptualize precision in psychotherapy remain at an early stage of development, and the field continues to face important conceptual and methodological challenges.

1.3. The Gap in Psychotherapy

Recent efforts to apply precision principles to psychotherapy have focused primarily on predicting treatment outcomes [12,13]. Machine learning (ML) models have been developed to identify patients at risk for non-response, dropout, or relapse, and attempts have been made to stratify patients into subgroups that may benefit from different therapeutic approaches [14,15].

Although these developments represent important steps toward individualized psychotherapy, they share a common limitation: most approaches focus on predicting outcomes rather than modeling the therapeutic process itself. Yet psychotherapy unfolds through a dynamic interpersonal dialogue between therapist and patient, in which shifts in behavior, cognition, emotion, and subjective experience emerge over time. Without conceptual frameworks capable of representing this interaction, it remains difficult to translate into potentially actionable guidance frameworks within the therapy session. Several authors have conceptualized psychopathology as a dynamic and interconnected system, in which symptoms and processes evolve over time through complex interactions (e.g., [16]). This perspective further highlights the need for frameworks capable of capturing process-level dynamics.

1.4. Aim of the Article

The present article proposes that advancing precision psychotherapy may benefit from moving beyond outcome prediction toward a deeper understanding of the psychotherapeutic process itself. Recent advances in artificial intelligence and natural language processing provide new opportunities to analyze psychotherapy dialogue and to quantify patterns of interaction between therapists and patients. Process models of mental activity have been proposed as one possible approach to structuring such analyses, although their application to psychotherapy remains largely conceptual.

In this paper, we introduce a dimensional representation of mental activity that is intended to allow psychotherapy interactions to be modeled as sequences of psychologically meaningful states. This process-oriented architecture is designed to provide a structured bridge between clinical theory and computational analysis, with the aim of supporting future efforts to analyze psychotherapy sessions in ways that remain interpretable to clinicians. By representing therapeutic dialogue as transitions across hierarchical dimensions of mental activity, the proposed framework is intended to offer a conceptually grounded and computationally tractable approach to modeling the psychotherapeutic process and to provide a basis for the systematic coding and computational analysis of therapeutic interactions.

2. The Concept of Precision Psychotherapy

Conceptual Definitions

Despite growing interest in precision psychotherapy, the concept itself remains relatively underdefined. In many contexts, the term broadly refers to attempts to personalize psychotherapeutic interventions based on patient characteristics, clinical profiles, or predicted treatment responses. This usage parallels common formulations of precision medicine, often summarized as delivering “the right treatment to the right patient at the right time” [17].

However, such formulations provide limited conceptual clarity. They describe the goal of precision without specifying the mechanisms through which precision is achieved. Consequently, the literature on precision psychotherapy encompasses a heterogeneous range of approaches, including predictive modeling of treatment outcomes, identification of transdiagnostic mechanisms of change, and digital technologies designed to monitor therapeutic processes [18].

Recent theoretical work [19] has proposed a more precise conceptualization by framing precision in psychotherapy as a reduction in uncertainty in clinical decision-making. From this perspective, research contributes to precision psychotherapy insofar as it narrows the range of plausible therapeutic options or increases confidence in the expected outcomes of particular interventions. Such reductions in uncertainty may occur at multiple levels, including diagnostic classification, treatment selection, prediction of treatment response, and understanding therapeutic mechanisms.

Importantly, this broader perspective allows precision psychotherapy to integrate multiple sources of information. Unlike narrower interpretations that emphasize biological markers, precision in psychotherapy may also emerge from psychological, relational, behavioral, and contextual data. Because psychotherapy operates primarily through communication and interpersonal interaction, many of the informational sources relevant to precision psychotherapy are inherently psychological and interactional.

Developing models that capture the structure and dynamics of therapeutic interaction may therefore represent an important frontier for precision psychotherapy. Such frameworks could enable new forms of measurement and analysis that complement predictive approaches and support the integration of artificial intelligence, process research, and clinical theory. In particular, models that render therapeutic dialogue structurally analyzable may allow AI systems to identify interaction patterns that are difficult to detect through traditional clinical observation alone. The dimensional framework proposed in this article represents one such attempt to formalize the structure of therapeutic dialogue in a manner that is both clinically meaningful and suitable for computational analysis.

3. Current Approaches to Precision Psychotherapy

The emerging field of precision psychotherapy has developed along several complementary research directions. Three lines of work have been particularly influential: predictive modeling using machine learning, mechanism-based approaches targeting specific processes of change, and digital augmentation through monitoring technologies.

3.1. Predictive Modeling and Machine Learning

One of the most prominent directions in precision psychotherapy involves the use of statistical modeling and machine learning techniques to predict treatment outcomes. These approaches aim to identify patterns in clinical and behavioral data that can forecast which patients are likely to benefit from specific interventions. By analyzing large datasets containing demographic, clinical, psychological, and sometimes biological predictors, predictive models attempt to estimate the probability that a given patient will respond to a particular therapeutic approach.

In recent years, machine learning methods have increasingly been applied to psychotherapy research for this purpose. These models can incorporate large numbers of predictors and detect complex and potentially nonlinear interactions among variables that may not be evident using traditional statistical methods. For example, Vieira et al. systematically reviewed and meta-analyzed machine learning studies predicting clinical response to cognitive-behavioral therapy (CBT) across multiple disorders, and highlighted the potential and current limitations of ML for individualized outcome prediction in psychotherapy [20]. Similar studies have emphasized the importance of robust cross-validation and methodological rigor, and have reported promising results in predicting treatment response, therapy dropout, and symptom trajectories [14,21].

Such predictive tools hold promise for improving treatment allocation and for identifying patients who may require more intensive or alternative interventions early in the course of therapy. In clinical settings, predictive analytics could support therapists in making more informed decisions about treatment planning and monitoring. However, many predictive models remain in early stages of development, and their generalizability and clinical utility continue to be evaluated [22].

3.2. Mechanisms of Change Approaches

A second major direction in precision psychotherapy research focuses on identifying the psychological mechanisms through which therapeutic change occurs. Rather than concentrating primarily on treatment outcomes, this line of research attempts to understand the processes that drive improvement during therapy and to tailor interventions accordingly.

The growing emphasis on mechanisms of change has been strongly articulated in the process-based therapy movement, which seeks to organize interventions around empirically supported change processes rather than diagnostic categories [23,24].

Mechanism-oriented approaches are often grounded in the observation that psychiatric disorders are highly heterogeneous. Patients who share a diagnostic category may differ substantially in the psychological processes that maintain their symptoms. For example, individuals with depression may vary in the relative contributions of maladaptive beliefs, interpersonal difficulties, emotion regulation deficits, or experiential avoidance to their condition. Precision psychotherapy, from this perspective, involves identifying the dominant mechanism contributing to a patient’s distress and selecting interventions that specifically target that mechanism [25].

This approach has been closely associated with the development of transdiagnostic frameworks that emphasize common psychological processes across diagnostic categories. By focusing on mechanisms such as cognitive biases, avoidance patterns, or emotion regulation strategies, clinicians may be able to design more flexible and individualized treatment plans [26]. Advances in measurement technologies, including ecological momentary assessment and digital behavioral tracking, have further expanded the possibilities for studying these mechanisms in real-world contexts [27,28].

Despite these advances, mechanism-based approaches often face practical challenges in identifying which processes are most relevant for a given patient at a particular moment during therapy, particularly in the absence of formal models linking mechanisms to the unfolding structure of therapeutic interaction.

3.3. Enhanced Psychotherapy and Digital Augmentation

A third line of work aims to enhance traditional psychotherapy through the integration of digital technologies and continuous monitoring systems. These approaches seek to improve treatment precision by providing therapists with more detailed and timely information about patient progress and therapeutic processes.

One influential development in this area is the use of feedback-informed treatment systems. In these systems, patients regularly complete brief assessments of symptoms, well-being, and therapeutic alliance, and the results are immediately available to therapists. Research has shown that such feedback systems may, under certain conditions, help identify patients at risk of deterioration or dropout and may improve treatment outcomes by enabling therapists to adjust their interventions accordingly [29,30].

Beyond symptom monitoring, digital tools increasingly allow for the collection of rich behavioral and contextual data. Smartphone-based assessments, wearable devices, and ecological momentary assessment methods can provide continuous information about mood fluctuations, activity patterns, sleep, and social behavior. These data sources can offer insights into patients’ experiences between therapy sessions and may help inform more responsive treatment strategies [31,32].

In addition, emerging technologies such as virtual reality interventions, digital self-management platforms, and AI-supported clinical decision systems are being explored as ways to augment psychotherapeutic care [33]. Together, these developments contribute to a growing vision of psychotherapy as a more dynamic, data-informed process in which therapeutic interventions can be continuously adapted based on ongoing assessment of patient needs.

3.4. The Process Gap in Precision Psychotherapy

Although the three lines of research described above represent important advances toward personalized psychotherapy, they largely focus on prediction, mechanisms, or monitoring outside the moment-to-moment structure of therapeutic interaction. Predictive models aim to forecast treatment outcomes, mechanism-based approaches seek to identify processes that drive change, and digital monitoring systems provide additional information about patient states between sessions. Yet comparatively little attention has been devoted to formal models that describe how therapeutic processes unfold within the session itself.

This limitation creates an important gap in the development of precision psychotherapy. Without frameworks capable of representing the internal structure of therapeutic dialogue, it remains difficult to translate predictive insights or mechanism-based hypotheses into systematic guidance for therapeutic action. Precision in psychotherapy ultimately depends not only on identifying which treatment may benefit a particular patient, but also on understanding how therapeutic interaction evolves moment by moment within the clinical encounter.

A variety of observational coding systems have been developed to study specific aspects of therapeutic interaction. Representative examples include the Core Conflictual Relationship Theme method (CCRT [34]), the Hill Counselor Verbal Response System (HCVRS; [35]), and the Motivational Interviewing Skill Code (MISC; [36]), among others. These frameworks have provided valuable tools for analyzing therapist behaviors, relational dynamics, and conversational patterns within psychotherapy sessions. However, they are primarily designed as research instruments for retrospective coding and typically focus on specific interactional elements rather than offering integrative models of psychological processing during therapy. The dimensional framework proposed here is intended not as a replacement for existing psychotherapy process models, but as a complementary representational structure aimed at organizing moment-to-moment therapeutic interaction in a modality-neutral and potentially computationally tractable manner.

3.5. AI-Based Analysis of Psychotherapy Process

Recent advances in artificial intelligence have opened new possibilities for studying psychotherapy processes at scale. In particular, developments in natural language processing (NLP) and machine learning enable the systematic analysis of psychotherapy transcripts and conversational dynamics within therapy sessions [37,38]. Because psychotherapy unfolds primarily through dialogue, computational analysis of therapeutic language provides a promising avenue for examining how psychological processes emerge and evolve during treatment. For example, applications of NLP in psychotherapy research have examined linguistic indicators of emotional expression, therapist responsiveness, and alliance processes within therapy sessions [39,40,41]. Such approaches allow researchers to analyze large numbers of therapy sessions and to detect patterns of interaction that may not be readily visible through traditional qualitative analysis alone [42].

However, the meaningful application of AI to psychotherapy research depends on conceptual frameworks capable of organizing therapeutic dialogue into psychologically interpretable categories. Machine learning models can detect statistical regularities in language data, but without theoretically grounded representations of psychological processes, these patterns remain difficult to interpret clinically. As a result, the development of computational approaches to psychotherapy increasingly requires models that structure therapeutic dialogue in terms of coherent forms of mental activity.

One possible strategy is to represent psychotherapy dialogue as sequences of psychological states expressed through speech. Within such frameworks, individual utterances can be interpreted as manifestations of different modes of mental activity, allowing therapy sessions to be modeled as dynamic trajectories of psychological expression. The dimensional framework introduced in the following section represents one example of such an approach.

4. A Dimensional Model of Therapeutic Interaction

The following section introduces a dimensional model of therapeutic interaction. The framework conceptualizes psychotherapy dialogue as movement across distinct forms of mental activity, providing a structured representation of the therapeutic process that may support computational analysis and AI-assisted process analysis in psychotherapy.

4.1. The Dimensional Model of Mental Activity

The Dimensional Model [43] conceptualizes mental life as organized across five interconnected dimensions: action, thought, emotion, experience, and being. Each dimension represents a distinct mode through which psychological activity becomes manifest in communication and behavior. Rather than constituting discrete categories, these dimensions form a nested structure of mental functioning through which subjective life can be expressed, interpreted, and transformed.

Action refers to intentional behavior and observable activities carried out by the individual. In psychotherapy dialogue, this dimension appears when patients describe events, actions taken, or plans for future behavior. Thought refers to articulated cognitive content, including interpretations, beliefs, explanations, and reflective reasoning about events or internal states. Emotion denotes identifiable affective states directed toward objects, people, or situations, such as anger toward a partner or fear about an anticipated outcome. Experience refers to inward-directed subjective states that may not yet be fully articulated or conceptually organized. These expressions often involve internal sensations, diffuse feeling states, or vague experiential atmospheres that precede more structured emotional narratives. Finally, Being represents the most fundamental level of psychological existence, referring to enduring aspects of identity, selfhood, and existential orientation that shape the individual’s range of psychological possibilities. Although the dimensional framework draws on insights from several psychotherapy traditions, it is intended as a modality-neutral representation of psychological expression in therapeutic dialogue.

Within this framework, the five dimensions form a dynamic architecture through which mental processes unfold. Psychological activity often appears as a movement across dimensions, in which experiences may become articulated as emotions, emotions may be reflected upon in thought, and thoughts may guide intentional action. Conversely, disruptions or defensive processes may interfere with such movement, limiting access to particular dimensions of mental life.

Within this architecture, transitions across dimensions follow a set of organizing tendencies that regulate how psychological expression unfolds in dialogue [43]. Transitions and interactions among dimensions are governed by five operational principles. Dominance refers to the tendency for one dimension to organize attention and expression at a given moment. Continuity holds that transitions typically occur between adjacent dimensions rather than through abrupt leaps (“dimensional leaps”) across multiple levels. Equilibrium reflects the psyche’s tendency to maintain a dynamic balance among dimensions in response to internal and external demands. Mutual attraction describes the interpersonal tendency for individuals in interaction to draw one another toward their own dimensional equilibrium. Flexibility refers to the capacity to move fluidly across dimensions, a capacity associated with psychological adaptability and resilience.

From this perspective, psychotherapy can be understood as a process that regulates dimensional organization and facilitates adaptive transitions across modes of mental activity. Therapeutic dialogue unfolds through shifts in dominant dimensions as patients and therapists explore events, meanings, emotions, and subjective experiences. The therapist’s task is therefore not only to address psychological content but also to recognize and support coherent movement across dimensions, enabling deeper integration of mental life.

For empirical or computational analysis, the dimensional model can be operationalized at the level of discrete speech segments or utterances within therapeutic dialogue. Each utterance can be classified according to the dominant mode of psychological expression it conveys (action, thought, emotion, experience, or being). Although elements from multiple dimensions may appear within a single statement, coding focuses on the dimension that organizes the utterance’s primary communicative function. In cases where multiple dimensions may plausibly apply to a single utterance, classification is based on the dimension that most directly organizes the speaker’s primary communicative intention within the interaction. The present coding approach is intended as a structured and exploratory framework for representing psychotherapy process rather than as a fully validated classification system. Future empirical work may further examine the reliability and reproducibility of dimensional coding procedures using inter-rater agreement methodologies. This approach parallels other psychotherapy process coding systems in which a single dominant category is assigned to each segment of dialogue. Such segmentation allows therapy sessions to be represented as sequences of dimensional expressions and transitions, enabling both human coders and computational systems to analyze patterns of dimensional movement, synchrony between therapist and patient, and disruptions in continuity across the course of the session.

As with other psychotherapy process coding systems, the reliability of dimensional classification can be evaluated through inter-rater agreement among trained coders, providing a basis for validating automated classification models trained on annotated psychotherapy dialogue. In empirical applications, coding could be performed at the level of discrete utterances or conversational turns within psychotherapy transcripts. Trained human coders can be used to annotate dialogue segments according to dimensional categories, allowing inter-rater reliability to be assessed (e.g., using Cohen’s kappa). These annotated datasets could then be used to train and evaluate machine-learning models capable of automatically classifying dimensional states in psychotherapy dialogue.

4.2. Implications for AI-Based Process Analysis

Once dialogue segments are coded according to dimensional categories, therapy sessions can be represented as temporal sequences of dimensional states, allowing computational analysis of transition probabilities, dimensional synchrony between therapist and patient, and patterns associated with therapeutic progress or rupture.

At a conceptual level, the proposed approach can be described as a multi-stage computational pipeline. First, psychotherapy transcripts serve as the primary input data. Second, human annotators label individual utterances according to the dimensional framework, generating annotated datasets. Third, supervised natural language processing (NLP) models can be trained on these annotated data to classify new dialogue segments into dimensional categories. Fourth, the output of these models consists of sequences of dimensional states across therapy sessions. Finally, these sequences can be analyzed to examine patterns such as transition probabilities, synchrony between therapist and patient, and disruptions in dimensional continuity.

Preliminary empirical work has begun to examine whether aspects of the dimensional framework can be operationalized and systematically studied within structured assessment contexts [44]. In a recent vignette-based study involving trainees and experienced clinicians, therapists’ microprocess-level clinical discrimination was examined through structured evaluations of alternative therapeutic responses organized according to dimensional criteria [45]. The study demonstrated the feasibility of articulating and measuring moment-to-moment clinical judgments within a structured framework, including the use of expert-consensus procedures, inter-rater reliability analyses, and sensitivity to structured exposure. Importantly, although trainees initially demonstrated lower baseline discrimination scores than experienced clinicians, they showed larger pre–post changes following structured exposure, resulting in more comparable post-assessment performance patterns. While preliminary and not indicative of equivalent clinical expertise or in-session performance, these findings raise the possibility that structured articulation of microprocess-level distinctions may help render aspects of clinical judgment more explicit and accessible within psychotherapy training contexts and process research.

Once such classifications are available, computational analyses could examine patterns such as the frequency of particular dimensions, the sequence of dimensional transitions, and the degree of synchrony between therapist and patient across sessions. For example, computational models could estimate transition probabilities between dimensions across therapy sessions, allowing researchers to examine whether effective therapies exhibit characteristic trajectories, such as gradual movement from experiential states toward articulated emotional meaning and reflective thought. Such transition patterns could potentially be examined as candidate process markers of therapeutic change.

Importantly, several components of this pipeline are currently feasible, including manual annotation of psychotherapy transcripts and supervised classification of utterances using existing NLP methods. Other components, such as real-time analysis during therapy sessions or direct clinical decision-support, remain prospective and require further empirical validation and technical development.

4.3. Clinical Example: Dimensional Mismatch and Premature Interpretation in Psychotherapeutic Dialogue

Clinical and process research in psychotherapy has repeatedly shown that therapeutic interventions that are not aligned with the patient’s current level of psychological processing may disrupt the therapeutic dialogue and generate momentary ruptures in the alliance. Early clinical observations by Gendlin [46] highlighted how patients often approach an implicit experiential state, what he termed the felt sense, which is initially vague, bodily, and not yet fully symbolized in language. When therapists respond to such experiential expressions with premature conceptual labeling or interpretation, the experiential process may collapse, and patients frequently report that the original feeling has disappeared. Related phenomena have been described in research on the therapeutic alliance by Safran & Muran [47,48], who demonstrated that alliance ruptures often emerge when therapist interventions fail to respond to the patient’s immediate experiential or relational state. Empirical process studies further support this observation. For example, Castonguay and colleagues [49] found that cognitive interventions delivered in the context of intense patient affect predicted poorer outcomes and deterioration in the therapeutic alliance.

From the perspective of the Dimensional Model, such disruptions can be understood as dimensional leaps. These leaps violate the first two principles governing dimensional movement- dominance and continuity. They do not allow the patient to remain within the dimension that is most salient at that moment and instead move away from it through an abrupt dimensional jump, bypassing the adjacent dimension. Patients frequently begin with an experiential expression, often bodily or affectively grounded, which may gradually unfold into articulated emotion and reflective meaning. When therapists intervene from a more distant dimension, such as conceptual interpretation, before this process has unfolded, the continuity of psychological processing is interrupted. The following clinical vignette illustrates this phenomenon. The dialogue between patient and therapist is presented in the Table below, with the corresponding dimensional category indicated for each utterance. The dimensional labels presented in this vignette are assigned according to the dominant expressive mode of each utterance, following the coding principles outlined in the previous section. When multiple dimensions may be inferred within a single utterance, classification is based on the dimension that most directly organizes the communicative function of the statement. It should be noted that alternative interpretations are possible, and the coding presented here (

Table 1. Example of dimensional continuity and rupture following a therapist’s dimensional leap.

Utterance	Speaker	Utterance	Dimension	Remark
1	Patient	I’m not sure how to say this… there’s just this tight feeling in my chest when I think about going back home.	Experience
2	Therapist	Stay with that for a moment. What does that feeling seem like from the inside?	None	(Question/facilitative intervention)
3	Patient	It’s strange… almost like something is pressing down.	Experience
4	Therapist	Pressing down?	None	(Question/facilitative intervention)
5	Patient	Yes… like I can’t quite breathe fully.	Experience
6	Therapist	What happens as you notice it right now?	None	(Question/facilitative intervention)
7	Patient	It gets a bit stronger when I talk about it.	Experience
8	Therapist	Stronger in your chest?	None	(Question/facilitative intervention)
9	Patient	Yes… it feels heavy… maybe a little sad.	Experience
10	Therapist	Sad.	Experience
11	Patient	Yes… but it’s not exactly sadness… it’s more like something is about to come up.	Experience
12	Therapist	Perhaps you are interpreting your reaction in a way that relates to unresolved issues with your father.	Thought
13	Patient	…No, that doesn’t feel right.	Experience	(Patient’s retraction)
14	Therapist	What doesn’t feel right about it?	None	(Question/facilitative intervention)
15	Patient	I don’t know… the feeling I was talking about is gone now.	Experience	(Patient’s retraction)

) is intended for illustrative purposes rather than as a formally validated coding exercise. The dimensional leap performed by the therapist, associated with a therapeutic rupture, is highlighted in bold (Table 1).

In this vignette, the patient initially operates within the dimension of experience, describing a bodily and pre-reflective state. The therapist initially supports the experiential exploration by encouraging attention to the unfolding sensation. However, when the therapist introduces an interpretive formulation (“Perhaps you are interpreting your reaction in a way that relates to unresolved issues with your father”), the intervention shifts abruptly into the dimension of thought, bypassing the gradual differentiation through which experience might have become articulated emotion and meaning (Figure 1). The patient responds by rejecting the interpretation and reporting that the original feeling has disappeared. From a dimensional perspective, the rupture emerges precisely at the point of this experience-to-interpretation leap, which interrupts the continuity of psychological processing.

An AI-based system trained to classify therapeutic dialogue according to the dimensional framework may offer a structured way to examine such dynamics. By coding segments of dialogue into dimensional categories, computational analysis could reveal patterns of dimensional movement, transitions, and disruptions across the course of a session. Such systems could analyze large collections of therapy sessions across therapists and treatment settings, identifying recurring patterns of interaction that may be associated with therapeutic success or failure. Such a system could operate asynchronously, analyzing transcripts after the session and providing clinicians or researchers with a structured map of dimensional dynamics, identifying, for example, moments of continuity, dimensional synchrony, or abrupt leaps that may correspond to therapeutic ruptures. In more advanced implementations, similar tools might also support synchronous analysis, operating during the session itself and offering real-time feedback about the evolving dimensional structure of the dialogue. At present, such applications remain hypothetical and exploratory. The current framework is intended primarily as a conceptual and research-oriented structure for analyzing psychotherapy process, rather than as a validated clinical decision-support system capable of detecting ruptures or guiding therapeutic interventions in real-world practice.

Taken together, the example illustrates how the combination of conceptually robust categorical frameworks (such as the Dimensional Model) with the computational power of AI may significantly enhance the precision with which psychotherapy processes are observed and understood, thereby opening new possibilities for refining therapeutic practice.

5. Clinical and Research Implications

The integration of artificial intelligence with process-based models of psychotherapy may have the potential to inform both clinical practice and psychotherapy research. If therapeutic interactions could be systematically represented and analyzed, new opportunities may emerge for improving therapist training, refining research methodologies, and developing more responsive clinical interventions. At the same time, the introduction of AI-assisted tools raises important ethical considerations that must be addressed to ensure that technological innovation supports rather than undermines the human foundations of psychotherapy.

5.1. Implications for Therapist Training

A process-based perspective on psychotherapy has important implications for how therapists are trained and supervised. Traditional training programs often focus on theoretical orientation and on the acquisition of specific intervention techniques associated with particular therapeutic schools. While such knowledge remains essential, process-based frameworks highlight the importance of recognizing and navigating the unfolding dynamics of therapeutic interaction.

Models that describe psychotherapy in terms of underlying psychological processes may provide therapists with a more structured way of understanding what occurs within therapy sessions [44]. Related preliminary empirical work has also begun to explore the potential relevance of structured dimensional process analysis within psychotherapy training contexts, including the articulation of microprocess-level clinical judgments through vignette-based evaluation tasks [45]. Future studies could further examine whether identifying shifts between different modes of mental activity (such as action, thought, emotion, or experiential dimensions) may help therapists track the evolving focus of the dialogue and adjust interventions in response to ongoing therapeutic processes. Such frameworks may therefore support the development of greater process sensitivity, enabling therapists to respond more flexibly to the needs of individual patients. For example, therapists in training may use dimensional coding of psychotherapy vignettes or session transcripts to examine moments of synchrony, dimensional continuity, or disruption within therapeutic dialogue. Such exercises may help render implicit clinical judgments more explicit and open to reflective discussion within supervision and training settings.

AI-assisted tools may, in principle, complement this training by providing feedback on therapy sessions based on computational analysis of therapeutic dialogue. Yirmiya & Fonagy, for example, critically examine the integration of generative AI into psychotherapeutic practice, focusing on its ability to simulate core therapeutic components such as empathy and mentalizing, and discuss both the potential and limitations of AI in replicating relational mechanisms essential to psychotherapy [50]. In the future, systems might be able to identify patterns in therapist–patient interaction, highlight moments of emotional engagement or disengagement, or detect shifts in the depth of psychological exploration. Used appropriately, such feedback systems could potentially enhance reflective practice and provide additional learning opportunities for therapists in training.

5.2. Implications for Clinical Trials

The integration of process-based models with AI-assisted analysis also has significant implications for psychotherapy research and clinical trial design. Traditional psychotherapy trials typically evaluate treatment effectiveness primarily through pre–post symptom measures or other outcome indicators. While these measures provide important information about treatment efficacy, they often offer limited insight into the mechanisms and processes through which change occurs.

Process-oriented approaches enable researchers to examine the internal dynamics of therapy sessions themselves. By analyzing patterns of interaction across sessions, researchers may be able to explore the possibility of identifying process markers associated with therapeutic improvement, stagnation, or deterioration. Such markers could provide earlier indicators of treatment progress than outcome measures alone. Within a dimensional framework [43], such analyses could focus specifically on patterns of dimensional transitions, therapist–patient synchrony across dimensions, and disruptions in dimensional continuity during therapy sessions.

Furthermore, incorporating process metrics into clinical trials may facilitate the development of more adaptive treatment designs. For example, real-time analysis of therapeutic dialogue may, in future applications, contribute to identifying patterns associated with non-response, although its role in guiding therapeutic adjustments remains to be established. Adaptive trial designs that integrate ongoing process monitoring may, if validated, contribute to more personalized and responsive treatment models.

5.3. Ethical Considerations

While the integration of AI into psychotherapy research and practice offers promising possibilities, it also raises a range of ethical challenges that must be carefully considered. Psychotherapy involves highly sensitive personal information, and the collection and analysis of therapy transcripts or recordings require strict safeguards to protect patient privacy and confidentiality. Any use of AI-based analysis must therefore adhere to rigorous standards of data protection and informed consent [51].

Another important ethical issue concerns the role of AI within the therapeutic relationship [52]. Psychotherapy is fundamentally a human interaction grounded in empathy, trust, and shared understanding. AI-assisted tools should therefore be designed to support therapists rather than replace human clinical judgment. The purpose of such technologies should be to provide additional insights or feedback that may enhance clinical decision-making, while preserving the therapist’s central role in interpreting and responding to the patient’s experience.

Finally, transparency and interpretability remain critical considerations in the development of AI-based systems for psychotherapy. Clinicians must be able to understand how computational tools generate their insights and recommendations. Black-box models that produce predictions without interpretable reasoning may undermine trust and limit their usefulness in clinical settings [53]. Ensuring that AI-assisted tools remain transparent, accountable, and aligned with clinical values will be essential for their responsible integration into mental health care.

In addition, governance structures may be required to oversee the development, validation, and clinical deployment of AI systems used in psychotherapy research, ensuring that such tools are evaluated according to clear standards of scientific validity, clinical safety, and ethical accountability.

Realizing this potential would require careful attention not only to technological innovation but also to the theoretical, clinical, and ethical foundations that guide the development of psychotherapy as a human-centered discipline.

6. Limitations

This work has several limitations that should be acknowledged.

First, the proposed dimensional framework is conceptual in nature and has not yet undergone systematic empirical validation. While the model is theoretically grounded and illustrated through clinical examples, its reliability, validity, and clinical utility remain to be established through empirical research.

Second, the coding of psychotherapy dialogue into dimensional categories involves an element of interpretive judgment. Although the framework proposes the use of dominant expressive modes and inter-rater agreement procedures, the assignment of dimensions may remain partly subjective, particularly for complex or ambiguous utterances. In addition, the present framework focuses primarily on verbal content and linguistic organization within therapeutic dialogue. Nonverbal and paralinguistic dimensions of communication, including prosody, pauses, vocal intensity, and rhythm, may also carry clinically meaningful information and represent important directions for future multimodal computational research.

Third, the application of natural language processing (NLP) methods to psychotherapy data faces important technical and practical limitations. Clinical language is often nuanced, context-dependent, and metaphorical, which may challenge current computational approaches and limit classification accuracy.

Fourth, the generalizability of the proposed framework across different therapeutic modalities, patient populations, and clinical settings remains uncertain. The model is intended as a modality-neutral representation, but its applicability across diverse contexts requires further investigation.

Finally, the translation of process-based computational analysis into clinically actionable tools remains a future direction. While the framework outlines a potential pathway toward AI-assisted analysis of therapeutic processes, its role in informing clinical decision-making has yet to be demonstrated.

7. Conclusions

Advances in artificial intelligence, particularly in natural language processing, create new opportunities for analyzing psychotherapy processes at scale. Yet the clinical usefulness of such technologies depends on theoretical frameworks capable of organizing therapeutic dialogue into psychologically interpretable forms. The dimensional model presented in this article illustrates one possible approach to this challenge. By conceptualizing psychotherapy dialogue as movement across distinct dimensions of mental activity (action, thought, emotion, experience, and being), the framework provides a structured representation of how psychological processes unfold within therapeutic interaction. Such representations allow therapeutic dialogue to be modeled as sequences of meaningful psychological states, creating a conceptual bridge between clinical theory and computational analysis.

Integrating predictive analytics with process-based models may therefore represent an important step in the evolution of precision psychotherapy. Rather than focusing exclusively on predicting which patients will respond to treatment, future research may increasingly seek to understand how therapeutic change unfolds within the moment-to-moment dynamics of therapy sessions. AI-assisted systems grounded in theoretically coherent process models could, if empirically validated, help identify patterns of dimensional movement, synchrony between therapist and patient, and disruptions in therapeutic continuity. Within this perspective, precision psychotherapy becomes not only a matter of predicting outcomes, but also of understanding psychotherapy processes with greater precision and clarity and, in future work, potentially supporting their systematic analysis.