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

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.

To be presented at EMNLP 2026. Code available at this http URL

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2609.04350 [cs.CL]

(or arXiv:2609.04350v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.04350 Focus to learn more arXiv-issued DOI via DataCite

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