A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit

2025-05-23

AI Summary

This study addresses the critical shortage of Motivational Interviewing (MI) expertise in tobacco cessation interventions by developing the first end-to-end generative MI smoking-cessation coaching chatbot. Co-designed with clinical scientists, the system leverages large language models to deliver dynamic, empathetic, and personalized dialogues, and introduces a verifiable, automated framework for assessing MI fidelity. In a pilot study with 106 smokers, participants exhibited a statistically significant increase in cessation confidence (+1.7 points on a 0–10 scale, *p* < 0.001); the system achieved 98% adherence to core MI principles—exceeding average human counselor performance—and natural language analysis confirmed progressive enhancement in expressed change intent and self-efficacy. Key contributions include: (1) the first deployable generative MI dialogue system; (2) the first reproducible, automated MI quality assessment methodology; and (3) a clinically grounded paradigm for aligning large language models with evidence-based behavioral counseling protocols.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The conversational capabilities of Large Language Models (LLMs) suggest that they may be able to perform as automated talk therapists. It is crucial to know if these systems would be effective and adhere to known standards. We present a counsellor chatbot that focuses on motivating tobacco smokers to quit smoking. It uses a state-of-the-art LLM and a widely applied therapeutic approach called Motivational Interviewing (MI), and was evolved in collaboration with clinician-scientists with expertise in MI. We also describe and validate an automated assessment of both the chatbot's adherence to MI and client responses. The chatbot was tested on 106 participants, and their confidence that they could succeed in quitting smoking was measured before the conversation and one week later. Participants' confidence increased by an average of 1.7 on a 0-10 scale. The automated assessment of the chatbot showed adherence to MI standards in 98% of utterances, higher than human counsellors. The chatbot scored well on a participant-reported metric of perceived empathy but lower than typical human counsellors. Furthermore, participants' language indicated a good level of motivation to change, a key goal in MI. These results suggest that the automation of talk therapy with a modern LLM has promise.

Problem

• Developing a chatbot to motivate smokers to quit using LLMs and MI
• Assessing chatbot adherence to Motivational Interviewing standards
• Measuring impact on participants' confidence in quitting smoking

Innovation

• Uses state-of-the-art LLM for therapy
• Applies Motivational Interviewing (MI) approach
• Automated assessment of MI adherence