Beyond Reflection: Affirmation as a Promising Behavioral Marker Associated with Quality in Text-Based Counseling

2026-08-27

AI Summary

研究通过分析KokoroChat数据集，发现肯定策略比反思更稳定地与高质量对话相关联，为咨询师培训和情感支持系统设计提供了实证依据。

Abstract

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.

Problem

• text-based counseling
• dialogue quality
• counselor behaviors
• Affirmation
• Reflection

Innovation

• Affirmation
• Dialogue Quality
• Cross-dataset Transfer