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Generation of Backward-Looking Complex Reflections for a Motivational Interviewing-Based Smoking Cessation Chatbot

Ash Tanuj Kumar¹, Cindy Wang¹, Alec Dong¹

¹Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada.

JMIR Mental Health

|September 26, 2024

Summary

This study developed a method using GPT-4 to generate backward-looking complex reflections (BLCRs) for a smoking cessation chatbot. The AI successfully created high-quality, therapeutic responses, improving accessibility for motivational interviewing (MI).

Keywords:

ChatGPT GPT-4 automated therapy cessation chatbot dialogue agent effectiveness large language models messages motivational interviewing natural language processing reflection generation reflections smoker smokers smoking smoking cessation therapy

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Area of Science:

Natural Language Processing (NLP)
Artificial Intelligence (AI) in Healthcare
Behavioral Science and Digital Therapeutics

Background:

Motivational interviewing (MI) is effective for smoking cessation but faces accessibility challenges.
A chatbot, MIBot, aims to provide MI sessions, focusing on reflective listening.
Backward-looking complex reflections (BLCRs) are a key MI technique not yet implemented in MIBot.

Purpose of the Study:

To develop and evaluate a method for generating BLCRs using large language models (LLMs).
To enhance the MIBot chatbot's capability for delivering MI-based smoking cessation support.

Main Methods:

Utilized GPT-4, an advanced LLM, with prompt engineering to generate BLCRs.
Developed prompts based on conversational history to elicit specific backward-looking reflections.
Tested the method on 150 reflections from 50 MIBot transcripts, with quality assessed by independent raters.

Main Results:

88% of the 150 generated BLCRs met acceptability criteria.
High inter-rater agreement (80%-88%) indicates reliable quality assessment.
The LLM approach demonstrated effectiveness in generating contextually relevant therapeutic reflections.

Conclusions:

The developed method is suitable for generating BLCRs in MI-style conversations.
An automated checker is recommended to filter out any remaining unacceptable reflections.
Highlights the potential of advanced LLMs for creating personalized, therapeutic digital interventions.