@inproceedings{mahmood-etal-2025-fully,
title = "A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit",
author = "Mahmood, Zafarullah and
Ali, Soliman and
Zhu, Jiading and
Abdelwahab, Mohamed and
Collins, Michelle Yu and
Chen, Sihan and
Zhao, Yi Cheng and
Wolff, Jodi and
Melamed, Osnat C. and
Minian, Nadia and
Maslej, Marta and
Cooper, Carolynne and
Ratto, Matt and
Selby, Peter and
Rose, Jonathan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1283/",
doi = "10.18653/v1/2025.findings-acl.1283",
pages = "25008--25043",
ISBN = "979-8-89176-256-5",
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."
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%0 Conference Proceedings
%T A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit
%A Mahmood, Zafarullah
%A Ali, Soliman
%A Zhu, Jiading
%A Abdelwahab, Mohamed
%A Collins, Michelle Yu
%A Chen, Sihan
%A Zhao, Yi Cheng
%A Wolff, Jodi
%A Melamed, Osnat C.
%A Minian, Nadia
%A Maslej, Marta
%A Cooper, Carolynne
%A Ratto, Matt
%A Selby, Peter
%A Rose, Jonathan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F mahmood-etal-2025-fully
%X 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.
%R 10.18653/v1/2025.findings-acl.1283
%U https://aclanthology.org/2025.findings-acl.1283/
%U https://doi.org/10.18653/v1/2025.findings-acl.1283
%P 25008-25043
Markdown (Informal)
[A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit](https://aclanthology.org/2025.findings-acl.1283/) (Mahmood et al., Findings 2025)
ACL
- Zafarullah Mahmood, Soliman Ali, Jiading Zhu, Mohamed Abdelwahab, Michelle Yu Collins, Sihan Chen, Yi Cheng Zhao, Jodi Wolff, Osnat C. Melamed, Nadia Minian, Marta Maslej, Carolynne Cooper, Matt Ratto, Peter Selby, and Jonathan Rose. 2025. A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25008–25043, Vienna, Austria. Association for Computational Linguistics.