| Challenge: | Existing methods to treat insomnia neglect conversational aspects, which plays a critical role in sleep therapy. |
| Approach: | They propose to develop conversational AI for a sleep coaching programme which is motivated by CBT-I treatment and provide an automated analytic system to support human experts. |
| Outcome: | The proposed system could interact naturally with a user and provide an automated analytic system to support human experts. |
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Benchmarking Large Language Models on Communicative Medical Coaching: A Dataset and a Novel System (2024.findings-acl)
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| Challenge: | Existing applications of natural language processing (NLP) focus on patient-centered services, but the potential of NLP to benefit inexperienced doctors remains unexplored. |
| Approach: | They propose a human-AI cooperative framework to assist medical learners in practicing communication skills during patient consultations. |
| Outcome: | The proposed framework enables medical learners to practice communication skills during patient consultations while a coach agent provides immediate, structured feedback. |
Exploring the Role of Mental Health Conversational Agents in Training Medical Students and Professionals: A Systematic Literature Review (2025.findings-acl)
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| Challenge: | This systematic review analyses 38 studies on AI-powered conversational agents in mental health education and training . traditional training methods provide valuable but expensive and inherently limited learning opportunities . early pioneers like Woebot and Wysa demonstrated a groundbreaking insight: machines could engage in meaningful therapeutic interactions. |
| Approach: | They analyse 38 studies on AI-powered conversational agents in mental health education and training . findings reveal that AI-based approaches dominate the field, with training as the application area being the most prevalent . |
| Outcome: | The systematic review of 38 studies on AI-powered conversational agents in mental health education and training (MHET) reveals that AI-based approaches dominate the field, with training as the application area being the most prevalent. |
Preparing Data from Psychotherapy for Natural Language Processing (L18-1)
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| Challenge: | mental health care is a demanding occupation, resulting in a severe gap in patient-centered care . a recent study shows that natural language processing can extract certain aspects of human-human communication. |
| Approach: | They propose to use data from psychotherapy sessions to help improve quality of care . they use feedback and cooperation annotations to assess quality of therapy sessions . |
| Outcome: | The proposed method aims to analyse psychotherapy data and assess its quality . it aims at identifying what qualifies for good feedback or cooperation in therapy sessions . |
Deep Learning for Conversational AI (N18-6)
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| Challenge: | Spoken Dialogue Systems (SDS) have great commercial potential . the advent of deep learning has led to significant advances in this area of NLP research . |
| Approach: | This tutorial will introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems. |
| Outcome: | This tutorial will familiarise researchers with the latest advances in spoken dialogue systems . the aim of the course is to encourage dialogue research in the NLP community . |
From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis (2025.findings-acl)
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| Challenge: | Problem-Solving Therapy (PST) is a structured psychological approach that helps individuals manage stress and resolve personal issues. |
| Approach: | They developed a framework for PST annotation using established PST Core Strategies and a set of novel Facilitative Strategies to analyze a corpus of real-world therapy transcripts to determine which strategies are most prevalent. |
| Outcome: | The proposed framework outperforms existing models and LLMs to identify the most prevalent strategies in a corpus of real-world therapy transcripts. |
CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy (2025.naacl-long)
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Mian Zhang, Xianjun Yang, Xinlu Zhang, Travis Labrum, Jamie C. Chiu, Shaun M. Eack, Fei Fang, William Yang Wang, Zhiyu Chen
| Challenge: | Existing research has explored mental health condition classifications, empathetic conversations, and chatbots designed for simple discourse structures. |
| Approach: | They propose a benchmark for systematic evaluation of cognitive behavioral therapy assistance using Large Language Models (LLMs). |
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Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond (2023.acl-tutorials)
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| Challenge: | Conventional conversation researches focus on the responseability of the system, such as dialogue context understanding and response generation, but overlook the design of an essential property in intelligent conversations, i.e., goal awareness. |
| Approach: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
| Outcome: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings (2022.coling-1)
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Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp, Bing Liu, Ben Gerber, Nikolaos Agadakos, Shweta Yadav
| Challenge: | Health coaching is cost-prohibitive due to its highly personalized nature. |
| Approach: | They propose to build a health coaching dialogue system that converses with patients . they propose to use simplified NLU and NLG frameworks and mechanism-conditioned empathetic response generation. |
| Outcome: | The proposed system generates more empathetic, fluent, and coherent responses . it outperforms the state-of-the-art in NLU tasks while requiring less annotations. |
“What’s Up, Doc?”: Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets (2025.findings-emnlp)
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Akshay Paruchuri, Maryam Aziz, Rohit Vartak, Ayman Ali, Best Uchehara, Xin Liu, Ishan Chatterjee, Monica Agrawal
| Challenge: | a growing number of people are seeking healthcare information from large language models via chatbots, yet the nature and inherent risks of these interactions remain unexplored. |
| Approach: | They use a curated dataset of 11K real-world conversations composed of 25K user messages to analyze user interactions across 21 health specialties. |
| Outcome: | The proposed dataset consists of 11K real-world conversations composed of 25K user messages. |
It’s Not under the Lamppost: Expanding the Reach of Conversational AI (2024.lrec-main)
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| Challenge: | Focused probes into the capabilities of language-based assistants easily reveal significant areas of brittleness that demonstrate large gaps in their coverage. |
| Approach: | They propose a process for collecting specific kinds of data to uncover these gaps and an annotation scheme for system responses. |
| Outcome: | The proposed system includes both Conventional and GenAI systems, including ChatGPT and Bard/Gemini. |