探究教学型教师与学生LLM智能体：遗传适应与检索增强生成在学习风格中的结合
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
AI总结：
本研究提出一个结合遗传算法与检索增强生成的LLM模拟框架，包含自我优化的教师智能体和异构学生智能体，以支持适应性教学模式的涌现，为教师培训提供测试平台。
摘要：
有效的教学需要调整教学策略以适应学生多样化的认知和行为特征，这是教育和教师培训中一个持续存在的挑战。虽然大型语言模型（LLM）作为模拟此类复杂教学环境的工具展现出前景，但当前的模拟框架在两个关键方面存在局限：（1）它们常将学生简化为静态的知识档案，（2）缺乏用于模拟教师根据学生反馈演变其策略的适应性机制。为解决这些不足，我们引入了一个新颖的模拟框架，该框架将基于LLM的异构学生智能体与自我优化的教师智能体相结合。教师智能体的教学策略通过遗传算法动态演化，使其能够基于多样化学习者的总体表现发现并优化有效的教学策略。此外，我们提出了Persona-RAG，一个检索增强生成模块，使学生的智能体能够检索适合其个人学习风格的知识。Persona-RAG在保持标准RAG基线检索准确性的同时，增强了个性化，这是模拟现实教育场景的关键因素。通过大量实验，我们展示了我们的框架如何在与不同学生群体互动时支持独特且可解释的教学模式的出现。我们的结果凸显了LLM驱动的模拟在指导适应性教学实践方面的潜力，并为在受控、数据驱动的环境中培训人类教育者提供了一个测试平台。
英文摘要：
Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teacher training. While Large Language Models (LLMs) offer promise as tools to simulate such complex pedagogical environments, current simulation frameworks are limited in two key respects: (1) they often reduce students to static knowledge profiles, and (2) they lack adaptive mechanisms for modeling teachers who evolve their strategies in response to student feedback. To address these gaps, \textbf{we introduce a novel simulation framework that integrates LLM-based heterogeneous student agents with a self-optimizing teacher agent}. The teacher agent's pedagogical policy is dynamically evolved using a genetic algorithm, allowing it to discover and refine effective teaching strategies based on the aggregate performance of diverse learners. In addition, \textbf{we propose Persona-RAG}, a Retrieval Augmented Generation module that enables student agents to retrieve knowledge tailored to their individual learning styles. Persona-RAG preserves the retrieval accuracy of standard RAG baselines while enhancing personalization, an essential factor in modeling realistic educational scenarios. Through extensive experiments, we demonstrate how our framework supports the emergence of distinct and interpretable teaching patterns when interacting with varied student populations. Our results highlight the potential of LLM-driven simulations to inform adaptive teaching practices and provide a testbed for training human educators in controlled, data-driven environments.
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