Brinck, E. A., et al. (2026).
Behavioral Sciences, 16(6), Article 1038.
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.
