JMIR Publications

ABSTRACT

Background:

Motivational interviewing (MI) is a counseling approach used in healthcare meant to promote behavior change by strengthening a patient’s intrinsic motivation. Recent advances in artificial intelligence (AI) present an opportunity to expand access to MI by delivering personalized behavioral support at scale. Simultaneously, MI has been increasingly utilized within Native communities, which experience significant health disparities including high rates of substance use, diabetes, and cardiovascular disease. Due to geographical isolation, Indigenous lands have limited access to healthcare and may particularly benefit from AI-enabled MI interventions that can deliver support beyond traditional clinical settings. However, the current landscape of research at the intersection of AI, MI, and Native health remains fragmented.

Objective:

To synthesize and characterize the existing literature on AI-enabled MI interventions in Native populations and identify emerging applications, research trends, and knowledge gaps.

Methods:

A scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology and reported according to the PRISMA Extension for Scoping Reviews (PRISMA-ScR). Searches were performed in PubMed, Scopus, and Google Scholar using terms related to three categories: motivational interviewing, artificial intelligence, and Native American populations. Eligible studies involved two or more of the following criteria: (1) evaluated an AI-enabled MI intervention, (2) evaluated an MI intervention among Indigenous populations, or (3) evaluated an AI-enabled health intervention among Native American populations. The studies were then screened and extracted on study design, sample, population, and purpose.

Results:

A total of 1,644 records were identified. After removal of 169 duplicates, 1,475 records underwent title and abstract screening. Full-text review was conducted for 130 articles, resulting in 36 studies meeting inclusion criteria (2.19%). Among these studies, 21 (58.3%) examined AI-delivered MI interventions, 11 (30.6%) focused on MI interventions in Native populations, and 4 (11.1%) evaluated AI-enabled health interventions for Native populations. Substance use was the most common intervention focus (21/36, 58.3%), primarily targeting smoking cessation and alcohol use, followed by healthy lifestyle interventions (4/36, 11.1%). AI-enabled MI interventions generally demonstrated high acceptability, engagement, and perceived empathy, while culturally adapted MI interventions were consistently reported as acceptable and culturally relevant within Native communities. No articles were identified that included both AI MI interventions and Indigenous populations.

Conclusions:

This scoping review highlights the growing role of AI in MI and its potential to support health behavior change in Indigenous communities. Although no existing studies have directly combined AI, MI, and Native populations, the existing literature suggests that this approach warrants further investigation as a strategy to promote healthy lifestyles and address health disparities. Early evidence indicates that technology assisted MI as well as culturally adapted MI interventions can improve accessibility, engagement, and adherence. However, these approaches have not yet been rigorously integrated or evaluated. Future research should focus on the effectiveness and long-term health outcomes of AI-enabled MI interventions specifically tailored for Native communities.