Can Motivational Interviewing be delivered using Artificial Intelligence chatbots? Evaluating the capability of...

To systematically evaluate the capability of the Large Language Model, Generative Pre-Trained Transformer (GPT-4o), to deliver Motivational Interviewing.

Large Language Models are a type of Artificial Intelligence trained to understand and generate human language. OpenAI's GPT-4o was prompted to conduct Motivational Interviews with simulated patient actors. The interview transcripts were analysed using the Motivational Interviewing Treatment Integrity code, which includes thresholds for assessing competency in Motivational Interviewing. Conversation sequences were furthermore examined to understand GPT-4o's patterned responses across Motivational Interviewing behaviours.

A total of 36 Motivational Interviewing transcripts were generated covering a range of chronic conditions and health behaviours. GPT-4o performed above 'good' competency thresholds for behaviour counts (Percent Complex Reflection and Reflection to Question ratio). Total Motivational Interviewing-Adherent behaviour counts were very high with very few Total Non-Motivational Interviewing-Adherent counts. GPT-4o did not perform as well on global competency scores, performing below 'fair' competency thresholds for both relational and technical skills. A distinct pattern was observed across the interviews where GPT-4o gradually shifted away from Motivational Interviewing towards attempts to persuade participants to change, which is inconsistent with the principles and processes of Motivational Interviewing.

GPT-4o has some capacity to mimic Motivational Interviewing behaviours but requires further augmentation to perform it with proficiency.

Using Artificial Intelligence has the potential to increase accessibility and reduce the costs of delivering Motivational Interviewing. Further research is needed to explore effective augmentation techniques before Large Language Models such as GPT-4o can be used to deliver Motivational Interviewing.