JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI), particularly large language models (LLMs), is increasingly used in digital health to support patient engagement and behavior change. One novel application is the delivery of motivational interviewing (MI), an evidence-based, patient-centered counseling technique designed to enhance motivation and resolve ambivalence around health behaviors. AI tools, including chatbots and virtual agents, have shown promise in simulating human-like dialogue and applying MI techniques at scale. However, the extent to which AI systems can faithfully replicate MI principles and generate meaningful behavioral outcomes remains unclear.

Objective:

This scoping review aimed to assess the scope, characteristics, and findings of existing studies that evaluate AI systems delivering motivational interviewing directly to patients. Specifically, we examined the feasibility of these systems, their fidelity to MI principles, and any reported outcomes related to health behavior change.

Methods:

We conducted a comprehensive search of five electronic databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) for studies published between January 1, 2018, and February 25, 2025. Eligible studies included any empirical design that used AI to perform MI with patients targeting a specific health behavior (e.g., smoking cessation, vaccine uptake). We excluded studies using AI solely for training clinicians in MI. Three independent reviewers conducted screening and data extraction. Extracted variables included study design, AI modality and type, health behavior focus, MI fidelity assessment, and reported outcomes. Data were synthesized narratively to map the evidence landscape.

Results:

Out of 1001 records identified, 8 studies met the inclusion criteria. Most were exploratory feasibility or pilot studies; only one was a randomized controlled trial. AI modalities included rule-based chatbots, large language models (such as GPT-4), and virtual reality conversational agents. Targeted behaviors included smoking cessation, substance use reduction, vaccine hesitancy, type 2 diabetes self-management, and opioid use during pregnancy. Across studies, AI-delivered MI was rated as usable and acceptable. Patients frequently described AI systems as “judgment-free” and supportive, which enhanced openness and engagement, particularly in stigmatized contexts. Expert evaluations of MI fidelity reported high alignment with MI principles in most cases. However, participants also noted a lack of emotional depth and limited perceived empathy. One study improved these perceptions by adjusting conversational pacing and content complexity. Only one study evaluated behavioral outcomes and found no statistically significant changes.

Conclusions:

AI systems, particularly those powered by LLMs, show promise in delivering motivational interviewing that is scalable, accessible, and perceived as nonjudgmental. While AI can replicate many structural aspects of MI and foster engagement, current evidence on its efficacy in driving behavior change is limited. More rigorous studies, including randomized controlled trials with diverse populations, are needed to assess long-term outcomes and to refine AI-human hybrid models that balance efficiency with relational depth.