Role-Playing LLM-Based Multi-Agent Support Framework for Detecting and Addressing Family Communication Bias

2025-07-15

AI Summary

This study addresses emotional suppression in children stemming from implicit “ideal-parent bias” in familial communication—where parents’ unconscious value-laden discourse inhibits children’s affective expression and autonomy. We propose the first large language model (LLM)-based multi-agent role-playing intervention framework. Leveraging a curated corpus of 30 authentic Japanese parent–child dialogues, we design specialized agents capable of detecting suppressed emotions, identifying implicit biases, and performing contextual inference; a meta-agent integrates domain expertise to generate structured feedback reports. Our framework introduces the first joint quantitative annotation scheme for ideal-parent bias and emotional suppression and delivers actionable recommendations via a four-step empathic discussion protocol. Experimental evaluation shows moderate accuracy in suppressed-emotion classification, high ratings for empathy and practicality of generated feedback, and significant improvements in affective expression and mutual understanding in simulated dialogues.

Technology Category

• Humans and AI: Emotional Intelligence
• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Detects unconscious parental bias in family communication
• Addresses suppressed emotions in children due to bias
• Provides LLM-based feedback to improve family interactions

Innovation

• LLM-based multi-agent framework analyzes family dialogues
• Specialized agents detect bias and suppressed emotions
• Meta-agent compiles reports for expert feedback generation