肯定比共情更可靠地预示心理咨询对话质量。
Beyond Reflection: Affirmation as a Promising Behavioral Marker Associated with Quality in Text-Based Counseling
- 分析日本专业咨询数据，发现肯定行为与对话质量关联更强
- 在多指标评估下，肯定的正向预测力显著优于共情
- 结果对培训系统设计有启发，适合心理支持领域研究者
尽管人工智能辅助文本心理咨询日益受到关注，但何种咨询师行为与高质量对话相关仍缺乏实证。现有研究多聚焦共情，借鉴动机访谈框架。本文基于由专业咨询师与实习生提供的大规模日文咨询数据集KokoroChat，新标注了咨询策略标签和来访者困扰水平，进行多层次分析。结果表明，在本研究采用的质量指标下，肯定行为比共情更一致地与会话质量相关。跨数据集迁移实验进一步显示，该质量信号在非专家支持的英文数据集ESConv中也部分可见。研究为咨询师培训与情感支持系统设计提供了实证依据。我们已在GitHub开源额外标注与实验代码：https://github.com/UEC-InabaLab/BeyondReflection。
原文摘要 · Abstract (English)
While AI-assisted text-based counseling is gaining attention, it remains empirically unclear which counselor behaviors are associated with higher dialogue quality. Existing research often focuses heavily on Reflection, borrowing frameworks from Motivational Interviewing. To address this gap, we conduct a multi-layered analysis using KokoroChat, a large-scale Japanese text counseling dataset conducted by professional counselors and trainees, newly annotated with counselor strategy tags and client distress levels. Our results show that, under the quality indicators used in this study, Affirmation is more consistently associated with session quality than Reflection among the analyzed strategies. Cross-dataset transfer experiments further suggest that this quality signal can be observed to some extent on ESConv, an English dataset with non-expert supporters. These findings provide empirical implications for counselor training and emotional support system design. We release the additional KokoroChat annotations and experimental source code at https://github.com/UEC-InabaLab/BeyondReflection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。