Well-being in family settings involves subtle psychological dynamics that conventional metrics often overlook. In particular, unconscious parental expectations, termed ideal parent bias, can suppress children's emotional expression and autonomy. This suppression, referred to as suppressed emotion, often stems from well-meaning but value-driven communication, which is difficult to detect or address from outside the family. Focusing on these latent dynamics, this study explores Large Language Model (LLM)-based support for psychologically safe family communication. We constructed a Japanese parent-child dialogue corpus of 30 scenarios, each annotated with metadata on ideal parent bias and suppressed emotion. Based on this corpus, we developed a Role-Playing LLM-based multi-agent dialogue support framework that analyzes dialogue and generates feedback. Specialized agents detect suppressed emotion, describe implicit ideal parent bias in parental speech, and infer contextual attributes such as the child's age and background. A meta-agent compiles these outputs into a structured report, which is then passed to five selected expert agents. These agents collaboratively generate empathetic and actionable feedback through a structured four-step discussion process. Experiments show that the system can detect categories of suppressed emotion with moderate accuracy and produce feedback rated highly in empathy and practicality. Moreover, simulated follow-up dialogues incorporating this feedback exhibited signs of improved emotional expression and mutual understanding, suggesting the framework's potential in supporting positive transformation in family interactions.

翻译：家庭环境中的幸福感涉及微妙的心理动态，而传统衡量标准往往忽视这些方面。特别是，无意识的父母期望——即理想父母偏见——可能抑制孩子的情感表达和自主性。这种抑制被称为压抑情绪，通常源于善意但受价值观驱动的沟通，从家庭外部难以察觉或应对。本研究聚焦于这些潜在动态，探索基于大语言模型（LLM）对心理安全的家庭沟通的支持。我们构建了一个包含30个场景的日语亲子对话语料库，每个场景均标注了理想父母偏见和压抑情绪的元数据。基于此语料库，我们开发了一个基于角色扮演LLM的多智能体对话支持框架，用于分析对话并生成反馈。专用智能体负责检测压抑情绪、描述父母言语中隐含的理想父母偏见，并推断情境属性（如孩子的年龄和背景）。元智能体将这些输出整合成结构化报告，随后传递给五个选定的专家智能体。这些智能体通过结构化的四步讨论过程，协作生成富有同理心且可操作的反馈。实验表明，该系统能够以中等准确度检测压抑情绪的类别，并产生在同理心和实用性方面均获高评价的反馈。此外，融入该反馈的模拟后续对话显示出情感表达和相互理解改善的迹象，表明该框架在支持家庭互动正向转变方面具有潜力。