AI教学法：面向人工智能智能体的对话式社会学习
AI Pedagogy: Dialogic Social Learning for Artificial Agents
AI总结：
受维果茨基社会文化理论启发，本研究提出AI Social Gym动态环境，通过AI师生智能体间的二元教学对话进行本体获取，证明混合方向交互显著提升LLM知识获取与应用能力，为AI训练提供补充路径。
摘要：
大型语言模型（LLMs）在处理大量离线数据集方面展现出了卓越的能力。然而，它们在在线获取和整合复杂知识时常常面临挑战。传统的AI训练范式主要基于监督学习或强化学习，类似于“皮亚杰式”的独立探索模型。这些方法通常依赖大型数据集和稀疏的反馈信号，限制了模型从交互中高效学习的能力。受维果茨基的社会文化理论启发，本研究探讨了社会中介学习范式在解决这些局限性方面的潜力。我们引入了一个被称为“AI Social Gym”的动态环境，在该环境中，AI学习者智能体与知识渊博的AI教师智能体进行二元教学对话。这些交互强调外部的、结构化的对话作为知识获取的核心机制，与仅依赖内部推理或模式识别的方法形成对比。我们的研究聚焦于在本体获取的背景下，不同的教学策略如何影响AI学习过程。实证结果表明，这种对话式方法——特别是那些结合自上而下的解释与学习者主动提问的混合方向交互——显著增强了LLM获取和应用新知识的能力，其表现优于单向教学方法和直接访问结构化知识（训练数据集中通常呈现的格式）。这些发现表明，将教学和心理学见解整合到AI和机器人训练中，可以大幅提升训练后的知识获取和响应质量。该方法为提示工程等现有策略提供了一条补充路径。
英文摘要：
Large Language Models (LLMs) have demonstrated remarkable capabilities in processing extensive offline datasets. However, they often face challenges in acquiring and integrating complex, knowledge online. Traditional AI training paradigms, predominantly based on supervised learning or reinforcement learning, mirror a 'Piagetian' model of independent exploration. These approaches typically rely on large datasets and sparse feedback signals, limiting the models' ability to learn efficiently from interactions. Drawing inspiration from Vygotsky's sociocultural theory, this study explores the potential of socially mediated learning paradigms to address these limitations. We introduce a dynamic environment, termed the 'AI Social Gym', where an AI learner agent engages in dyadic pedagogical dialogues with knowledgeable AI teacher agents. These interactions emphasize external, structured dialogue as a core mechanism for knowledge acquisition, contrasting with methods that depend solely on internal inference or pattern recognition. Our investigation focuses on how different pedagogical strategies impact the AI learning process in the context of ontology acquisition. Empirical results indicate that such dialogic approaches-particularly those involving mixed-direction interactions combining top-down explanations with learner-initiated questioning-significantly enhance the LLM's ability to acquire and apply new knowledge, outperforming both unidirectional instructional methods and direct access to structured knowledge, formats typically present in training datasets. These findings suggest that integrating pedagogical and psychological insights into AI and robot training can substantially improve post-training knowledge acquisition and response quality. This approach offers a complementary pathway to existing strategies like prompt engineering
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
