# CALM-IT: Enhancing Synthetic Motivational Interviewing Dialogue Quality with Conversational Dynamics

arXiv: https://arxiv.org/abs/2601.10085 · HTML: https://mlsanity.com/paper/2601.10085/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles](https://mlsanity.com/paper/2407.00870/) (2024)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory](https://mlsanity.com/paper/2407.03103/) (2024)
- [Evaluating Coherence in Dialogue Systems using Entailment](https://mlsanity.com/paper/1904.03371/) (2019)
- [Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances](https://mlsanity.com/paper/2106.02227/) (2021)
- [MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance](https://mlsanity.com/paper/2503.13509/) (2025)
- [GPTCoach: Towards LLM-Based Physical Activity Coaching](https://mlsanity.com/paper/2405.06061/) (2024)
- [Consistent Client Simulation for Motivational Interviewing-based Counseling](https://mlsanity.com/paper/2502.02802/) (2025)
- [CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration](https://mlsanity.com/paper/2502.02807/) (2025)
- [Deep Learning Mental Health Dialogue System](https://mlsanity.com/paper/2301.09412/) (2023)
- [Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations](https://mlsanity.com/paper/2505.20201/) (2025)
- [Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy](https://mlsanity.com/paper/2410.07147/) (2024)
- [A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit](https://mlsanity.com/paper/2505.17362/) (2025)
- [Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations](https://mlsanity.com/paper/2506.03941/) (2025)
- [GDPR Compliant Collection of Therapist-Patient-Dialogues](https://mlsanity.com/paper/2211.12360/) (2022)

## Built on by (most cited first, 3 of 3)

- [LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback](https://mlsanity.com/paper/2605.30273/) (2026)
- [ODRA: Synthesizing Cognitive Behavioral Therapy Sessions with Structured Chain-Of-Thought and Dynamic Patient Resistance](https://mlsanity.com/paper/2608.04524/) (2026)
- [PeerPen: AI-Assisted Writing for Online Mental Health Peer Support](https://mlsanity.com/paper/2609.14886/) (2026)

## Abstract (arXiv)

Conversational dynamics describe how rapport, emotion, and goals change over a dialogue. These changes are important in therapeutic conversations, where they can influence when and how therapists intervene. However, large language model (LLM)-based synthetic therapeutic dialogue generators rarely track how these properties evolve together, which may limit the quality of longer conversations. We introduce CALM-IT, a dual-agent framework that tracks client states and therapist-side inferences to guide strategy selection and utterance generation. We evaluate synthetic Motivational Interviewing (MI) dialogues using automated Motivational Interviewing Treatment Integrity (MITI) ratings, conversational metrics, and comparisons across dialogue lengths, with ratings from four licensed psychologists providing support for key findings. Across 8,232 dialogues covering 686 grounded scenarios, four frameworks, and three target lengths, CALM-IT achieves the highest mean scores on all four MITI 4.2 global ratings and shows the smallest aggregate change in quality between short- and long-form conditions. CALM-IT also achieves the highest simulated client redirection acceptance rate (64.3%), with its strongest redirections followed by increased change talk and reduced sustain talk. Ablation experiments show that removing conversational dynamics components reduces dialogue quality. Together, these results suggest that explicitly modeling conversational dynamics can improve the quality and consistency of long-form synthetic MI dialogues.