Evaluating motivational interview quality using large language models and hidden Markov models.

ABSTRACT:

Background

Motivational Interviewing (MI) is a counseling approach that promotes behavior change by eliciting "change talk" and minimizing "sustain talk." Traditional methods for assessing MI quality, such as manual coding, are labor-intensive, subjective, and difficult to scale. This study introduces an automated framework integrating large language models (LLMs) and Hidden Markov Models (HMMs) for evaluation of MI session quality.

Aims

This study evaluates the effectiveness of an LLM-HMM framework in predicting MI session quality and examines motivational state transitions in high- and low-quality sessions.

Method

A dataset of 40 MI sessions was analyzed. Client utterances were classified and numerically scored by an LLM based on their intention toward or away from

SUBMITTER: Lim K 

PROVIDER: S-EPMC12487504 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature