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.

翻译：利用大型语言模型（LLMs）模拟微妙的人类社会动态仍然是一个重大挑战，尤其是在实现心理深度和一致的角色行为方面，这对于高保真度训练工具至关重要。本文介绍了TACLA（基于交互分析情境的LLM智能体），这是一种新颖的多智能体架构，旨在克服这些局限性。TACLA整合了交互分析（TA）的核心原则，通过将智能体建模为一个由不同的父母、成人和儿童自我状态组成的协调系统，每个状态都有其自身的模式记忆。一个协调器智能体根据情境触发因素和智能体的生活脚本，优先激活自我状态，确保心理上真实的响应。在一个教育场景中验证，TACLA在学生智能体中展示了现实的自我状态转换，有效地模拟了基于不同教师干预策略的冲突升级和降级。评估显示了高度的对话可信度，并证实了TACLA创建动态、基于心理的社会模拟的能力，推动了教育和更广泛领域有效AI工具的发展。