Generative Artificial Intelligence Chatbots for Motivational Interviewing: A Scoping Review From System Design to Intervention Outcomes (Preprint)
Abstract
Background
Motivational interviewing (MI) is a collaborative communication approach used to elicit autonomous motivation in health-related behavior change. Generative artificial intelligence (GenAI) provides new opportunities to deliver MI through conversational systems, but evidence on how these systems are designed, assessed, and translated into interventions remains fragmented.
Objective
This scoping review aimed to characterize the current evidence on GenAI-MI chatbots across system design, safety measures, MI quality, user perceptions, and intervention outcomes.
Methods
We conducted a scoping review in accordance with PRISMA-ScR. Studies published or publicly available from January 2015 to June 2, 2026 were identified through PubMed, Web of Science, Scopus, PsycINFO, DBLP, IEEE Xplore, ACM Digital Library, arXiv, and ACL Anthology. Eligible studies used generative AI to generate MI-related chatbot responses or counselor utterances. Data were extracted using a predefined extraction framework and synthesized descriptively.
Results
Forty-seven reports comprising 48 studies were included. Twenty studies (41.7%) focused on system design without direct participant use, whereas 28 (58.3%) involved direct interaction with a GenAI-MI chatbot. Most systems were text based and disembodied, and 23 of 48 studies (47.9%) incorporated dynamic adaptation. Safety measures were unevenly reported, with privacy and data protection (20/48, 41.7%) and safety-oriented content generation (17/48, 35.4%) more commonly described than automated (7/48, 14.6%) or human (6/48, 12.5%) risk monitoring. Among studies involving direct participant use, 21 of 28 (75.0%) reported informed consent or user education. Thirty of 48 studies (62.5%) assessed MI quality using observer- or client-based evaluations. Existing observer and client evaluations generally suggested that GenAI-MI chatbots could produce MI-consistent interactions. User perceptions were generally favorable, particularly for empathy, usability, helpfulness, and intention to use, although measurement approaches were heterogeneous. Eighteen of 48 studies (37.5%) reported intervention outcomes, including applications in physical activity, smoking cessation, and alcohol or substance use. Most intervention studies involved a single session, and only 3 of 18 (16.7%) evaluated repeated use over 10 days to 4 weeks. Positive findings were reported more consistently for short-term motivation outcomes than for sustained behavioral or functional change.
Conclusions
Current evidence suggests that GenAI-MI chatbots can deliver MI-consistent interactions that are generally perceived favorably by users, but evidence supporting sustained behavioral or functional change remains limited. Future research should strengthen runtime safety monitoring, standardize MI quality assessment, and use longer-term comparative designs with behavioral and functional outcomes to determine whether short-term motivational changes could translate into meaningful intervention effects. CLINICALTRIAL OSF Registries xzw4g; https://osf.io/xzw4g
