Therapist Positionality: A Cornerstone of Antiracist Clinical Practice

Ashley, W. (2025).

Open Journal of Social Sciences, 13(11), 58–66.

https://doi.org/10.4236/jss.2025.1311004

Abstract

Therapist positionality is a foundational principle of antiracist clinical practice, yet it remains underemphasized in practitioner education, training, and ongoing professional development. Positionality refers to the interplay of a therapist’s intersecting identities, including race, gender, class, sexuality, ability, and professional status, and how these shape power, perception, and relational dynamics. This article examines the role of positionality across clinical, collegial, and supervisory relationships, offering strategies to promote reflexivity, cultural humility, and relational accountability. Using positionality, clinicians can move beyond cultural competence into an ongoing, embodied practice of cultural humility, ethical engagement, and cultural responsiveness, particularly with clients and colleagues from marginalized communities.

Here are some thoughts:

Ashley argues that therapist positionality, defined as the critical examination of how a clinician’s intersecting identities, power, and privilege influence the therapeutic relationship, is essential to moving beyond traditional notions of therapist neutrality toward genuine antiracist and culturally responsive practice. Moving beyond superficial disclosures, the paper details how authentic positionality requires ongoing intrapsychic self-reflection and interpersonal accountability across clinical, collegial, and supervisory dynamics. By explicitly naming power differentials and replacing the myth of the objective practitioner with embodied cultural humility, clinicians can foster greater trust, psychological safety, and relational repair with clients and colleagues from marginalized communities.

Think First, AI Second: Why Human Judgment Still Matters in Psychology

In this episode of Beyond the Couch, Dr. Ernest Wayde sits down with Dr. John Gavazzi, board-certified clinical psychologist, ethics educator, and former President of the Pennsylvania Psychological Association, to explore how psychologists can responsibly integrate artificial intelligence into clinical practice without compromising professional judgment or quality of care. 

Drawing decades of experience in ethics, mental health law, and clinical decision-making, Dr. Gavazzi explains why AI should enhance, not replace, the thinking process of psychologists. He introduces the concept of "Think First, AI Second," arguing that clinicians must remain the originators of thought, using AI only after applying their own expertise and clinical reasoning. 

The conversation explores the growing use of AI by both clinicians and clients, examining why people are turning to AI for emotional support, how psychologists can adapt when clients bring ChatGPT into therapy, and what must be preserved as AI becomes increasingly embedded in mental health care. Dr. Gavazzi also discusses cognitive offloading, automation bias, ethical accountability, AI literacy, and practical ways clinicians can redesign their workflows while maintaining professional competence. 

Ultimately, this episode reinforces that while AI is becoming an invaluable tool for psychology, human connection, ethical responsibility, and clinical judgment remain irreplaceable. 

Takeaways 

-Psychologists Should Think First and Use AI Second. 

-AI Can Support Clinical Work, But It Cannot Replace Human Judgment. 

-Cognitive Offloading Can Weaken Clinical Skills Over Time. 

-AI's Fluency Should Never Be Mistaken for Accuracy. 

-Clients Are Already Bringing AI Into Therapy Sessions. 

Development and Efficacy of an Ethical Decision-Making Tool for Training Clinical Psychologists

Hunt, M. G., et al. (2024).

Training and Education in Professional Psychology,

18(4), 284–296. https://doi.org/10.1037/tep0000467

Abstract

We developed an ethical decision-making tool to assist in training clinical psychologists that had four goals: (a) to mirror the way psychologists naturally think about ethical dilemmas; (b) to facilitate thorough analysis by using a simple, guided checklist format; (c) to provide quick access to intuitively organized explanations and examples of each major domain of ethical challenges and principles; and (d) to facilitate good documentation and record-keeping of the decision-making process. Study 1 surveyed clinical psychologists and asked them to list the top 10–15 ethical considerations they encounter in practice, and then used those categories to help develop the tool. In Study 2, having access to two different versions of the tool significantly improved the quality of analysis of a standardized clinical vignette, particularly for individuals still in training. In Study 3, a streamlined version of the tool significantly improved the quality of graduate trainees’ analysis of the standardized clinical vignette and was faster to use. In Study 4, we demonstrated the efficacy of the tool in helping trainees analyze ethical dilemmas they had actually experienced. In Study 5, faculty at doctoral programs who teach graduate ethics courses provided dilemmas and then graded the responses of trainees who analyzed them either with or without the tool. Analyses completed with the tool received far better grades on average than those completed without the tool. This work is the first that we know of to develop an evidence-based, empirically supported tool for training future psychologists in ethical practice.

Impact Statement

This study describes the development of a novel ethical decision-making tool for use in training clinical psychology graduate students. The study shows that the tool helps trainees improve their analysis of complex ethical dilemmas that arise in clinical practice.

The EDM tool is here.

Practical Frameworks for Integrating Artificial Intelligence in Forensic Psychology

Rilen, S. (2026).

Journal of Forensic Psychology Research

and Practice, 1–39.

https://doi.org/10.1080/24732850.2026.2630383

The article is paywalled. Please contact the author for a copy of this article.

Abstract

The American Psychological Association (APA) provides broad guidance for generative Artificial Intelligence (gAI) use, yet forensic practice requires a specialized context for adversarial settings. This article proposes a four-step operational framework for ethical gAI integration grounded in forensic admissibility. It establishes operational mandates and readiness assessments followed by a risk-stratification model for tool selection distinguishing convergent from divergent tasks. It differentiates substantive from peripheral uses to guide disclosure decisions, and addresses admissibility considerations under Daubert and Frye. Sample disclosure language and visual aids help experts preserve credibility through methodological verification and systematic procedural rigor, rather than algorithmic trust.

Here are some thoughts:

The article establishes that while generative AI offers significant potential to enhance forensic psychology, its integration must be governed by a strict, four-step framework that prioritizes practitioner accountability, methodological transparency, and data security. Central to this is the principle of non-delegation of cognition, which mandates that forensic experts retain full ownership of all analytical reasoning and conclusions, using gAI only as an augmentative tool rather than a substitute for professional judgment. All gAI output must be treated with a zero-trust verification approach, requiring independent validation against primary sources to guard against hallucinations, omissions, and algorithmic bias. Furthermore, the framework draws a critical distinction between substantive use—which materially influences opinions and requires proactive disclosure in court reports—and peripheral use, which involves administrative support and only requires informed consent. Given the adversarial legal context, practitioners must demonstrate technical competence to explain gAI's probabilistic nature and limitations, ensuring their methodology can withstand scrutiny under Daubert or Frye standards. Data privacy is paramount, necessitating enterprise-grade tools with robust security pillars, including data quarantine and encryption, to preserve legal privilege. Ultimately, the article emphasizes that forensic practitioners are the gatekeepers of their methodology, and by embracing rigor, transparency, and continuous self-assessment, they can harness gAI's benefits while safeguarding the integrity of the justice system.

Privilege in the room: Training future psychologists to work with power, privilege, and intersectionality within the therapeutic relationship.

Wright, A. J., et al. (2025).

Psychotherapy, 62(1), 82–89.

https://doi.org/10.1037/pst0000563

Between the racial reckoning of 2020 and wider spread policy development that is explicitly homophobic and transphobic, there have been consistent and resurgent calls for clinicians to address aspects of power and privilege in psychotherapy. This is especially important in a field that continues to be largely White, cisgender, and heterosexual (not to mention abled, socioeconomically privileged, and privileged in many other aspects of human diversity). However, too few models for how to accomplish this in actual practice are offered in the literature. Further, while there is little guidance for clinicians on how to address power, privilege, and intersectionality in the therapy room, there is even less direction for how to train those learning to be clinicians to do this from the start. The purpose of this article is to translate existing knowledge into a framework for supervisors to guide trainees’ application in psychotherapy. The article provides an overview of social location, including an analytic framework, as well as a set of practical steps for supervisors and trainees.

Impact Statement

Question: This article addresses how to train future psychologists to address issues of power, privilege, and intersectionality in their clinical work utilizing a structural frame. Findings: In addition to foundational definitions and concepts, the article provides guidance for supervisors, trainees, and clinicians on how to address power, privilege, intersectionality, and structural dynamics in clinical and supervisory practice. 

Meaning: Being vigilant and deliberate in addressing power, privilege, and intersectionality in clinical practice is crucial for building successful therapeutic and supervisory relationships, so employing the strategies shared will ultimately enhance clinical outcomes and create deeper understanding of the impact of structural oppression on the therapy enterprise. Next Steps: Training programs and supervisors must be vigilant and held accountable for engaging in the challenging work of addressing power, privilege, and intersectionality, utilizing a structural frame, to enhance clinical outcomes. 

A pdf can be found here.

The SHAPE Model: A Metacognitive Framework for Ethical Decision-Making

Gavazzi, J. (2026).

The Pennsylvania Psychologist, (86)2, 32-35.

Clinical Impact Statement

What Problem Does This Address for Clinicians?

When facing urgent, complex ethical dilemmas—such as managing high-risk safety concerns, unexpected disclosures, or boundaries in high-stress settings—clinicians often default to quick intuition because traditional ethical decision-making models are too lengthy or rigid to use in the moment.

How Does This Help in Everyday Practice?

The SHAPE model (Scrutinize, Hypothesize, Analyze, Perform, Evaluate) gives clinicians a fast, reliable mental framework to slow down just enough to avoid blind spots. It builds directly on a therapist’s clinical instincts while adding crucial guardrails:

• Prevents Knee-Jerk Actions: Prompts clinicians to step back and evaluate multiple options before acting under stress.

• Catches Unintended Consequences: Uses explicit safety checks (Contraindication Rule) to identify potential harms to the client or therapeutic alliance before a plan is executed.

• Works Under Pressure: Adapts gracefully to time-sensitive clinical scenarios without compromising ethical standards.

Bottom Line for Patient Care:

Using SHAPE helps therapists translate abstract ethics codes into practical, defensible, and compassionate clinical actions. Over time, reflecting on past decisions (Evaluate) sharpens clinical judgment, protects client welfare, and strengthens the therapeutic alliance.

A pdf is here.

An AI Perspective on Counseling Supervision

Brinck, E. A., et al. (2026).

Behavioral Sciences, 16(6), Article 1038. 

https://doi.org/10.3390/bs16061038

Abstract

The increased use of technology-assisted distance counseling practices is one result of COVID’s impact on behavioral health, including in counselor education and the delivery of supervision. First, technology-assisted distance supervision needed for “real time” communication grew. Furthermore, there is an emergence of artificial intelligence (AI) technologies that have the potential to contribute to aspects of supervision; however, current evidence remains emerging, context-dependent, and at times mixed, warranting cautious interpretation of their effectiveness. The article offers an overview of using AI in clinical supervision, examines the benefits and potential concerns of AI from different perspectives, and considers the significance of using AI in counseling supervision. The role of AI is discussed as applied to counseling supervision including the use of AI tools, such as chatbots and reasoning AI, to detect and track sessions, note behavioral and emotional cues, aid/monitor communication and feedback, while also attending to ethical and legal consideration for its use. The article will report a range of benefits for supervisors and trainees using AI—for example, by enhancing data-driven supervision decisions, analyzing feedback trends, providing more efficient administrative monitoring, flexible/remote support, skill development, and promoting ethical decisions and self-reflection. Special attention is given to the challenges of using AI in supervision, including risks of undervaluing intuition and qualitative insights, potential for algorithms to reinforce systemic biases, risks of replacing human interaction, as well as non-compliance with HIPAA, FERPA, and ethical guidelines in data storage and privacy. The article will discuss privacy concerns, depersonalized feedback, and increased judgment-driven anxiety despite needed empathy when using AI as a tool for clinical supervision. Recommendations will also be offered for effective, ethical integration of AI in counseling supervision.

Here are some thoughts:

This is a comprehensive review, and its most useful feature for a clinical audience is how thoroughly it maps existing professional guidance onto the specific problem of AI-assisted supervision. The authors walk through the NBCC's 2024 clinical tenets and the ACA's 13 recommendations in detail, which gives the piece real practical value as a reference document, something a supervisor could actually consult when drafting an informed consent form or a supervision contract that addresses AI use. The case example of Lexi and Jalen, where AI-flagged session notes surfaced a pattern of the trainee talking over a client's frustration, is a nice illustration of AI functioning as intended: not replacing supervisory judgment but generating material for the supervisor to process relationally with the supervisee.

The empirical base here is thinner than the framework suggests, and the authors are reasonably candid about this.

The most clinically substantive contribution is the discussion of the supervisory working alliance (SWA) as the issue actually at risk. The authors argue that AI-mediated feedback, delivered without the moderating effect of a trusted relationship, risks landing as more punitive or deficit-focused than the same content delivered by a supervisor who knows the trainee's developmental stage and history. That's a sharper articulation than most AI-and-supervision pieces manage, and it connects naturally to the Integrated Developmental Model framework they invoke early on: a supervisee's readiness to receive AI-flagged feedback without a supervisor's relational buffering plausibly varies by developmental level in ways this piece gestures at but doesn't fully develop.

Where I'd flag some limitation: the literature search was explicitly broad and non-systematic ("AI in human services" across a handful of databases), which the authors acknowledge as a scope limitation given how new this area is. And several of the ethical and legal considerations, informed consent language, data ownership, HIPAA and FERPA compliance, are more thorough in their treatment of what should happen than in documenting what is currently happening in supervision practice. The piece reads as a strong, well-organized synthesis and practical guide rather than an evidence base, which is an entirely fair thing for a review in this stage of the literature to be, but worth naming for readers expecting empirical weight.

The Role of Artificial Intelligence in Clinical Psychology: How AI and NLP Systems Are Reshaping Psychological Interventions. A Systematic Review

Orrù, L., & Mannarini, S. (2026). 

Clinical Psychology & Psychotherapy, 

33(2), e70242. 

https://doi.org/10.1002/cpp.70242

Abstract

Artificial Intelligence (AI) technologies are rapidly evolving and their integration into psychological practices has progressively expanded, offering new tools for diagnosis, treatment and therapeutic monitoring. This review examines the transformative role of AI, particularly Natural Language Processing (NLP) systems, in reshaping clinical psychology and digital mental health interventions (DMHIs). In particular, it explores how AI and NLP can facilitate human-machine interaction in therapy by analysing how language is used within clinical conversations and providing personalized, real-time interventions. Following PRISMA guidelines, a systematic review of literature from 2019 to 2025 identified 17 studies that met inclusion criteria, emphasizing AI's use in psychological assessment and intervention. The review focuses on two key aspects: the functions and applications of NLP-based systems in clinical practice and the advantages and benefits they offer for both psychologists and patients. Findings suggest that NLP-driven AI systems enhance both patient engagement and clinician efficiency, offering scalable, cost-effective solutions that improve access and personalization. However, challenges remain, including ethical concerns around data privacy, lack of standardization, limited generalizability across disorders and reduced human empathy. Moreover, current systems are primarily designed for well-defined conditions like anxiety and depression, with limited applicability to complex or comorbid psychological presentations. This review underscores the importance of supervised, ethically governed AI implementation. While AI holds substantial promise in augmenting clinical psychology, its success depends on maintaining human oversight, ensuring transparency and establishing shared scientific and ethical standards across the psychological community.

Summary

• Through the systematic review of the existing literature regarding the use of AI in clinical psychology, practitioners could know how AI and NLP systems are (re)shaping psychological interventions to improve them for digital mental health (DMH).
• The study describes characteristics, features and tasks of AI systems in clinical psychology, the main techniques currently adopted, from which practitioners can find information to choose the best tools for their work objectives, adapting to specific individual psychological needs.
• Practitioners could evaluate AI systems' impact on DMH promotion: beneficial effects of AI systems for patients' mental health and related challenges. Indeed, while on one hand the implementation of AI in clinical psychology has demonstrated significant improvements in psychological well-being, on the other hand some studies have reported less conclusive findings.

The unintended negative consequences of artificial intelligence use for psychologists

van Zyl, L. E. (2026).

Frontiers in Psychology, 17, Article 1729050.

https://doi.org/10.3389/fpsyg.2026.1729050

Abstract

The rapid integration of artificial intelligence (AI) into psychological practice is transforming how psychologists reason, document, and deliver care. While benefits such as improved diagnostic accuracy and administrative efficiency are widely recognized, the unintended consequences for psychologists themselves remain poorly understood. This commentary synthesizes recent empirical and conceptual literature to critically examine six domains through which sustained AI use may adversely affect psychologists’ professional functioning: (a) cognitive functioning and professional competence, (b) professional identity and meaning, (c) ethical reasoning and legal challenges, (d) interpersonal and social functioning, (e) data governance and vendor lock-in, and (f) personal wellbeing. The analysis draws on a narrative review of peer-reviewed sources from psychology, medicine, computer science, and organizational behavior, selected for their relevance to professional impacts of AI. Evidence suggests that cognitive offloading and automation bias may erode diagnostic reasoning and clinical judgment, moral deskilling and value misalignment can undermine ethical autonomy, and technostress, data dependence, and vendor lock-in pose growing threats to practitioner wellbeing and professional sovereignty. These dynamics indicate that AI operates not merely as a technical aid but as a transformative social and psychological actor that reshapes how psychologists think, decide, and relate to their work. The paper argues for the urgent development of evidence-based safeguards, practitioner education, and regulatory frameworks to ensure that AI enhances rather than erodes human competence and ethical reflection.

Here are some thoughts:

One of the more original contributions here is the concept of "AI-specific impostor syndrome." It differs from classic impostor syndrome in an important way: classic impostor syndrome involves discounting effort that was genuinely spent, while this variant arises because the effort really was minimal, so the resulting achievement feels hollow by comparison. The author grounds this in effort justification theory, which gives the idea some conceptual weight, though the supporting evidence (a finding that roughly half of AI users believe the models they use are smarter than they are) is drawn from the general population rather than from psychologists specifically. It is a theoretically motivated proposal at this point, not yet a measured clinical phenomenon.

The section on ethics and liability is likely the most practically relevant for practitioners. The "attributability gap" and "moral buffer" concepts describe a real bind: clinicians remain legally responsible for AI-influenced decisions even as they may feel less psychologically responsible for them, since the recommendation felt like it came from the system rather than their own judgment. The related "liability sink" problem is worth sitting with too, the idea that a psychologist could be exposed to liability both for using AI inappropriately and for failing to use it when it has become standard practice among peers. That double bind reflects how far regulatory and insurance frameworks currently lag behind clinical adoption.

One caveat worth keeping in mind while reading: this is a perspective commentary rather than a systematic review, and the author is upfront about that. But the six-domain structure and the density of citations can create an impression of more empirical settledness than actually exists. A fair amount of the argument extrapolates from adjacent fields, medicine, education, general knowledge work, into psychology specifically, a leap the author flags but which is easy to miss on a first read. It is best approached as a well-organized, hypothesis-generating map of where the risks might lie, rather than a set of confirmed findings.