用心理分析框架让AI角色更真实地互动，提升教育模拟效果。
TACLA: An LLM-Based Multi-Agent Tool for Transactional Analysis Training in Education
- 将心理分析中的父母/成人/儿童三种人格建模为多智能体系统。
- 能根据教师干预策略真实模拟冲突升级或化解过程。
- 适合教育场景中需要深度社会互动的AI训练工具开发者。
利用大语言模型（LLM）模拟复杂的人类社交动态仍面临挑战，尤其在心理深度和一致人格表现方面。本文提出TACLA（基于事务分析的LLM多智能体系统），通过将智能体建模为具有独立记忆模式的父母、成人和儿童三种人格状态，并由协调代理根据上下文触发和个体生命剧本决定人格激活顺序，实现心理层面的真实响应。在教育场景中验证表明，学生智能体能真实表现出人格状态转换，有效模拟不同教师干预策略下的冲突升级与化解。评估显示对话可信度高，证明TACLA具备构建动态、心理基础坚实的社交模拟能力，推动教育等领域高效AI工具的发展。
原文摘要 · Abstract (English)
Simulating nuanced human social dynamics with Large Language Models (LLMs) remains a significant challenge, particularly in achieving psychological depth and consistent persona behavior crucial for high-fidelity training tools. This paper introduces TACLA (Transactional Analysis Contextual LLM-based Agents), a novel Multi-Agent architecture designed to overcome these limitations. TACLA integrates core principles of Transactional Analysis (TA) by modeling agents as an orchestrated system of distinct Parent, Adult, and Child ego states, each with its own pattern memory. An Orchestrator Agent prioritizes ego state activation based on contextual triggers and an agent's life script, ensuring psychologically authentic responses. Validated in an educational scenario, TACLA demonstrates realistic ego state shifts in Student Agents, effectively modeling conflict de-escalation and escalation based on different teacher intervention strategies. Evaluation shows high conversational credibility and confirms TACLA's capacity to create dynamic, psychologically-grounded social simulations, advancing the development of effective AI tools for education and beyond.
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