A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit (2025)

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.

## introduction the remarkable rise in the capability of large language models (llms) gives hope that they could be used to provide many kinds of mental health talk therapy. indeed, one can simply ask for such help from an online llm and possibly receive good help @xcite . since this is a medical intervention, it should be grounded in evidence that shows its effectiveness. our goal is to automate a specific type of talk therapy focusing on the problem of tobacco addiction § corresponding author: jonathan.rose@utoronto.ca with the specific goal of moving ambivalent smokers towards the decision to quit. ambivalent smokers know that smoking is bad for them but continue smoking because of its positive effects and because they don't spend much time contemplating their smoking behaviour @xcite @xcite . more than 50% of all smokers are in this ambivalent state @xcite , and so moving even a small fraction of these towards the decision to quit could have a major impact. the motivational interviewing (mi) talk therapy approach @xcite is often employed by counsellors to guide smokers away from their ambivalent state towards the decision to quit. this decision is a key precursor for any successful attempt to quit @xcite . there has been significant activity in recent years on automating talk therapy in many domains, including the use of mi to help in smoking cessation @xcite @xcite . @xcite , the predecessor of the present work, developed mibot v5.2 which showed that a partially scripted and partially generative chatbot could significantly change smokers' readiness to quit. however, scripting with limited generation restricts the natural flow of conversation, thereby preventing full utilization of mi elements. @xcite show the effectiveness of a fully-generative chatbot focused on alcohol use. as well, more complete mi administered by human counsellors has shown a much greater impact @xcite . this, together with the potential availability of always-accessible, lower-cost counselling, forms the motivation for this work. in this paper, we describe the design and measurement of a single, large prompt of a state-of-the-art llm-based chatbot called mibot 1 . a key to our ap-proach is that expert mi clinicians and researchers participated in designing the prompt and evaluating the chatbot. we iteratively evolved the prompt with the help of mi experts, llm-simulated smokers, and humans role-playing as smokers. mibot was then tested on smokers recruited online (for pay) to measure both the effect on their confidence to quit and the quality of the conversations in four ways: the key contributions of this paper are: this paper is organized as follows: the next section describes prior work in the area of automated bots for smoking cessation. see appendix g for a comprehensive list of all previous mi chatbot iterations. unless otherwise noted, mibot refers to mibot v6.3a. mi using therapeutic chatbots (and their evaluation). section 3 describes the clinician-informed iterative design of mibot. section 4 discusses the methods of measurement and recruitment of human smokers. section 5 presents the results and discussion, and section 6 concludes. ## conclusion our study suggests that a fully generative mi-based chatbot could assist smokers in moving toward a decision to quit smoking. the mibot chatbot was shown to increase most participants' confidence in their ability to quit smoking -a strong predictor of successful cessation @xcite . a validated automated assessment indicated that participants' language reflected motivation towards change, another key goal of mi therapy. furthermore, the automated counsellor is exceptionally mi-compliant. we also showed that the chatbot achieves a good level of perceived empathy as reported by users. we plan to extend this work by continuing to iterate on the chatbot prompt and employing more sophisticated mi strategies. we anticipate a need to exert more control over the chatbot's text generation. furthermore, we will create extended conversations over multiple sessions and explore their effective-ness. we plan to conduct a randomized controlled trial to compare the effectiveness of a single session with our chatbot against a simpler digital intervention. finally, we plan to integrate the chatbot within an end-to-end smoking cessation mobile application to aid smokers throughout their entire quitting journey.