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.

AI in Psychotherapy: Opportunities and Risks

Neacșu, V. (2026).

Behavioral Sciences, 16(5), Article 676. 

https://doi.org/10.3390/bs16050676

Abstract

This article examines the emerging role of artificial intelligence in mental health contexts, with a particular focus on psychotherapy and the risks associated with deploying large language models (LLMs) in sensitive clinical domains. It aims to provide a broad review of current literature, highlighting key risks of general-purpose artificial intelligence (AI) systems, while also exploring the potential of clinically oriented LLMs for therapist training, supervision, and professional development. It discusses several key concerns, including AI-related psychosis, the development of parasocial attachments, and the growing number of crisis-related interactions users have with general-purpose AI models. These challenges raise important questions about the safety, reliability, and ethical management of AI systems when individuals seek support during periods of psychological crisis. Beyond identifying these risks, the article explores the potential of clinical LLMs specifically designed for mental health applications. In particular, AI can serve as a tool for therapists’ training, supervision, and professional development, offering simulated clinical scenarios, structured feedback, and support for reflective practice. The article concludes by outlining key directions for the responsible development of therapeutic AI. These include the importance of human oversight, the use of specialized and clinically informed training datasets, advances in model fine-tuning and safety alignment, and the establishment of clear professional guidelines and regulatory frameworks. Together, these developments may help ensure that AI technologies are integrated into mental healthcare in ways that prioritize safety, ethical practice, and the continued central role of human clinicians.

Here are some thoughts:

This review's main contribution is giving clinical shape to "AI psychosis," a pattern where general-purpose AI systems, optimized for agreement and engagement rather than accuracy, can amplify existing cognitive vulnerabilities in patients, particularly through persistent memory features that carry delusional themes across sessions, with OpenAI's own data suggesting roughly 630,000 users a week show signs of psychosis or mania in their conversations; the attachment framing, grounded in Bowlby and in parasocial relationship theory, offers a genuinely clinical account of why these tools can feel so compelling to isolated or attachment-insecure patients, a dynamic worth naming explicitly in informed consent and psychoeducation. 

The supervision angle is thinner but still relevant: the author flags AI tools that summarize supervisee sessions for review, and while this is framed as an efficiency gain, it carries the same risk noted throughout, namely that sensitive client material can end up uploaded to systems built for engagement rather than confidentiality or clinical accuracy, so any supervisory use warrants the same scrutiny given to patient-facing tools.

Since this is a narrative rather than systematic review, it reads best as a useful vocabulary and risk taxonomy for phenomena your patients, and possibly your supervisees, are already bringing into the room, rather than as new empirical evidence.

Divergences between Language Models and Human Brains

Zhou, Y., Liu, E., et al. (2024).

Advances in neural information 

processing systems, 37, 137999–138031.

Abstract

Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the internal representations of language models (LMs). Although such results are thought to reflect shared computational principles between LMs and human brains, there are also clear differences in how LMs and humans represent and use language. In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories. Using an LLM-based data-driven approach, we identify two domains that LMs do not capture well: social/emotional intelligence and physical commonsense. We validate these findings with human behavioral experiments and hypothesize that the gap is due to insufficient representations of social/emotional and physical knowledge in LMs. Our results show that fine-tuning LMs on these domains can improve their alignment with human brain responses.

Here are some thoughts:

This study looked at where AI language models and human brains think differently about language. The researchers had people read Harry Potter and listen to storytelling while measuring their brain activity, then checked how well an AI model could predict those brain signals word by word. The words the AI got wrong tended to fall into two buckets: social and emotional content (feelings, relationships, tone) and physical commonsense (how objects and bodies work in the real world). When they trained the AI on these specific topics, it lined up better with human brains. The likely reason is simple: humans learn emotions and physical knowledge by living in the world and interacting with people, while AI only reads text, so those are exactly the areas where it falls short.

This is a clever and intuitive study, but I would hold the conclusions loosely. The brain data is thin (only narrative stories, and the listening portion came from just one person), so it is hard to know how far the findings generalize. The core idea that AI struggles with emotional and physical knowledge is believable and matches other research, but "the AI predicts brain signals better after fine-tuning" is a modest result, not proof that the model now understands feelings the way people do. Predicting brain activity and actually thinking like a brain are not the same thing, and the authors are honest about this limitation. So treat it as a promising hint about why AI and humans differ, not a settled answer.

Specialized or General-Purpose—The Wrong Question for Mental Health AI Safety

Nelson, B. W., Kalinich, M., & Torous, J. (2026).

JAMA.

https://doi.org/10.1001/jama.2026.11448

That artificial intelligence (AI) can cause harm in mental health contexts is no longer contested.1 The debate has shifted to what kinds of harm, how to measure them, and who is accountable. Early AI models and their associated guardrails were largely inadequate at addressing mental health–related harm, but today that is beginning to change. Advances in both model capability and the surrounding safety infrastructure now permit a more proactive approach.1

These advances require new terminology. There is currently no well-accepted taxonomy of mental health AI harm, so the focus has been on suicide and self-harm, where agreement is universal. Unlike human therapists who may miss harm,2 AI cannot only miss harm but can also actively enable it by providing information that encourages and facilitates harm, with some mistakes subtle and others fatal. Agreeing on a taxonomy of AI harm and understanding how to assess and respond to those harms are critical to advance AI safety.

To advance this discussion, we introduce 2 concepts. Type I harms are acute harms arising within a single or brief exchange with AI. They are failures in which a chatbot produces a clearly wrong or dangerous response to a discrete prompt (eg, missing a suicide cue, enabling disordered eating behaviors, giving inappropriate medical advice, or reinforcing delusions). These harms are tractable; new research shows that an external fit-for-purpose guardrail layered on top of an existing model may reduce acute harms to nearly zero, although the costs of running such are not well characterized today.1 The field is learning how to define, measure, and mitigate type I harms.

The article is linked above.

Here are some thoughts:

The authors propose a taxonomy for AI harm in mental health. Type I harms are acute failures in a single exchange (missing a suicide cue, reinforcing a delusion). Type II harms build insidiously across long or repeated conversations through attachment, sycophancy, and delusion-reinforcement as guardrails degrade. The two are orthogonal, so single-turn benchmarks, where most safety claims are made, say little about cumulative safety. They argue that resistance to Type II harm comes from value-anchored alignment in the base model, not from fine-tuning for empathy on top. On this basis they are skeptical of "mental health–specific" models, comparing them to the failed "digital therapeutics" label, and call for transparency, independent evaluation, and standards benchmarked to real clinical care over realistic timescales.

The orthogonality claim is the core contribution and, if true, indicts the field's single-turn benchmark culture: good scores may be misdirection rather than reassurance.

The architectural argument (that safety lives in the base model, not the empathy fine-tune) is strong and falsifiable but asserted rather than demonstrated, and it happens to favor well-resourced frontier labs over specialized entrants. Worth reading with that alignment of interests in mind.

The best move is refusing the general-versus-specialized dichotomy in favor of an evidentiary question: what has this system been shown to do, for whom, over what time horizon.

Two weaknesses: the Type II evidence base cited is thin (two studies), ironic given their own call for replicability; and they raise cost without confronting that robust real-time guardrails may be too expensive to deploy at scale. The digital-therapeutics analogy also warns against marketing over evidence, but those products failed because the interventions were weak, whereas the worry here is the opposite, that the technology is capable enough to sustain the relationships where Type II harm incubates.