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

Summary

A new study introduces a prediction task: can we tell early whether volunteer mental-health crisis counselors will eventually improve at steering conversations toward positive outcomes? The authors find that tracking how counselors adapt after struggling with specific kinds of conversational moments predicts long-term skill growth better than models trained directly on full transcripts.

SourcearXiv Computational LinguisticsAuthor: Vivian Nguyen, Lillian Lee, Elizabeth A. Olson, Cristian Danescu-Niculescu-Mizil
Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors
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[Submitted on 3 Sep 2026]

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

View a PDF of the paper titled Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors, by Vivian Nguyen and 3 other authors

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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.

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

Subjects:

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

Cite as: arXiv:2609.04350 [cs.CL]

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

https://doi.org/10.48550/arXiv.2609.04350

arXiv-issued DOI via DataCite

Submission history

From: Vivian Nguyen [view email] [v1] Thu, 3 Sep 2026 18:14:04 UTC (650 KB)

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Key points and analysis

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Key points

  • New task: predict early whether crisis counselors will improve at guiding conversations toward positive outcomes.
  • Core insight: how people learn to handle specific difficult conversation moments is especially revealing of future improvement.
  • Method identifies initially challenging moments, captures counselors' adaptations when re-encountering similar moments, and learns which early changes predict improvement months or years later.
  • The adaptation-focused approach outperforms baselines that learn directly from conversation transcripts.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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