Evidence map›Paper›PMID 42352871›Full record

ReviewBehavioral sciences (Basel, Switzerland)2026

An AI Perspective on Counseling Supervision.

Emily A Brinck, James L Soldner, Hung Jen Kuo, Scott A Sabella, Trenton J Landon, Charles P Bernacchio, Elizabeth A Boland

Abstract readReview
In one paragraph

Review in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Emily A BrinckWisconsin Center for Education Research, University of Wisconsin-Madison, Madison, WI 53706, USA.ORCID 0000-0002-6007-5624
James L SoldnerSchool for Global Inclusion and Social Development, University of Massachusetts Boston, Boston, MA 02125, USA.ORCID 0000-0002-4657-0485
Hung Jen KuoDepartment of Counseling, Educational Psychology, and Special Education, Michigan State University, East Lansing, MI 48824, USA.ORCID 0000-0001-8365-4760
Scott A SabellaDepartment of Counseling, School, and Educational Psychology, State University of New York at Buffalo, Buffalo, NY 14260, USA.ORCID 0000-0002-4630-5454
Trenton J LandonDepartment of Special Education and Rehabilitation Counseling, Utah State University, 2865 Old Main Hill, Logan, UT 84322, USA.ORCID 0000-0001-5064-4128
Charles P BernacchioDepartment of Counselor Education, University of Southern Maine, Portland, ME 04104, USA.ORCID 0009-0005-5593-6564
Elizabeth A BolandDepartment of Health and Community Studies, Western Washington University, Everett, WA 98201, USA.ORCID 0009-0005-8902-5769

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligenceethical and legal issuessuperviseesupervisionsupervisor

Identifiers

PMID42352871
PMCPMC13295318

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.