Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors

• URL: http://arxiv.org/abs/2609.04350v1
• Date: Thu, 03 Sep 2026 18:14:04 GMT
• Title: Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors
• Abstract summary: This work introduces the task of predicting, early in a conversationalist's career, whether they will eventually improve at steering conversations toward positive outcomes.<n>Our central insight is that people may struggle with particular kinds of moments in a conversation, and that what is especially revealing of their likelihood of future improvement is how they learn to handle those moments over time.<n>We operationalize this insight by designing a method that identifies the types of moments a counselor initially struggles with, captures how they adapt their response when they re-encounter similar moments in subsequent conversations, and learns which early adaptations predict improvement months or even years later.
• Score: 8.306192406610428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How do people learn to become better conversationalists? This question is especially important in the context of mental-health counseling, where conversational skills are essential, yet volunteer counselors often have limited access to supervision and structured feedback. Understanding how counselors develop their ability to steer conversations toward positive outcomes -- and identifying early which counselors are (not) on track to improve -- can help prioritize support for the counselors who need it most. In this work, we introduce the task of predicting, early in a conversationalist's career, whether they will eventually improve at steering conversations toward positive outcomes, and demonstrate the feasibility of this task in the case of volunteer mental-health crisis counselors. Our central insight is that people may struggle with particular kinds of moments in a conversation, and that what is especially revealing of their likelihood of future improvement is how they learn to handle those moments over time. We operationalize this insight by designing a method that identifies the types of moments a counselor initially struggles with, captures how they adapt their response when they re-encounter similar moments in subsequent conversations, and learns which early adaptations predict improvement months or even years later. While this future-prediction task is challenging, our counselor-adaptation approach yields better results than baselines that learn directly from the conversation transcript.

Related papers

• Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations [11.315381025450373]
  We introduce an unsupervised computational method for detecting pivotal moments as they happen.<n>Our approach relies on the intuition that a moment is pivotal if our expectation of the outcome varies widely depending on what might be said next.<n>We then use our framework to explore the relation of the counselor's response during pivotal moments with the eventual outcome of the session.
  arXiv  Detail & Related papers  (2025-06-04T13:31:58Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy [9.654703213945467]
  We introduce a measure of the extent to which a certain utterance immediately redirects the flow of the conversation. We apply this new measure to characterize the development of patient-therapist relationships over multiple sessions in an online therapy platform. Our analysis reveals that patient control of the conversation's direction generally increases relative to that of the therapist as their relationship progresses.
  arXiv  Detail & Related papers  (2024-10-09T17:54:41Z)
• Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach [58.88422314998018]
  Emotional support conversation (ESC) aims to provide emotional support (ES) to improve one's mental state. Existing works stay at fitting grounded responses and responding strategies which ignore the effect on ES and lack explicit goals to guide emotional positive transition. We introduce a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation.
  arXiv  Detail & Related papers  (2023-07-16T09:58:44Z)
• Understanding Client Reactions in Online Mental Health Counseling [11.12088513389927]
  Previous NLP research on counseling has mainly focused on studying counselors' intervention strategies rather than their clients' reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors' strategies and client reaction behaviors. Our study shows how clients react to counselors' strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions.
  arXiv  Detail & Related papers  (2023-06-27T09:39:54Z)
• Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback [46.70617195649979]
  CARE is an AI-based tool to empower and train peer counselors through practice and feedback.<n> CARE helps diagnose which counseling strategies are needed in a given situation and suggests example responses to counselors during their practice sessions.
  arXiv  Detail & Related papers  (2023-05-15T19:48:59Z)
• Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform [35.9642101732025]
  This paper seeks to bridge the gap by mapping peer-counselor chat-messages to motivational interviewing techniques. We study the impact of MI techniques on the conversation ratings to investigate the techniques that predict clients' satisfaction with their counseling sessions. This work provides a deeper understanding of the use of motivational interviewing techniques on peer-to-peer counselor platforms.
  arXiv  Detail & Related papers  (2022-11-09T20:25:33Z)
• Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy [83.61404191470126]
  We propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors. The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.
  arXiv  Detail & Related papers  (2022-04-07T14:11:31Z)
• TalkTive: A Conversational Agent Using Backchannels to Engage Older Adults in Neurocognitive Disorders Screening [51.97352212369947]
  We analyzed 246 conversations of cognitive assessments between older adults and human assessors. We derived the categories of reactive backchannels and proactive backchannels. This is used in the development of TalkTive, a CA which can predict both timing and form of backchanneling.
  arXiv  Detail & Related papers  (2022-02-16T17:55:34Z)
• Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards [18.460035325229683]
  We develop an unsupervised methodology to quantify how counselors manage balance. Our main intuition is that if an utterance can only receive a narrow range of appropriate replies, then its likely aim is to advance the conversation forwards. By applying this intuition, we can map each utterance to a continuous orientation axis that captures the degree to which it is intended to direct the flow of the conversation forwards or backwards.
  arXiv  Detail & Related papers  (2020-05-08T18:00:27Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.