将AI助教模块化，让大模型更符合教育规律，避免误导学习。
Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance
- 拆分助教功能为多个模块，分阶段介入解题过程。
- 模块化设计使教学引导更透明可控，减少认知干扰。
- 适合教育科技开发者和关注AI伦理的教师使用。
AI聊天机器人在教育中的广泛应用将深刻改变学习方式，但其负责任部署成为关键挑战。尽管大型语言模型（LLMs）可访问教育科学相关资料，却缺乏对教学理念的自觉遵循，可能削弱学习者的迁移能力、批判性思维或创造力。本文提出一种面向习题解答的代理型AI助教架构，旨在促进教育中更负责任的AI应用。基于对负责任教育系统的核心需求分析，我们指出单一整体式方案存在结构性缺陷，主张采用模块化代理架构。具体设计多个针对不同解题阶段的功能模块，融入针对性教学建议，使学生学习过程更具可控性、透明性和可监督性。
原文摘要 · Abstract (English)
The widespread adoption of AI chatbots in education will drastically change learning, making responsible deployment a critical concern. While large language models (LLMs) might have access to sources discussing insights from educational sciences, they are not particularly inclined to adhere to pedagogical concepts, risking negative effects on the learning process, such as a loss of transfer capabilities, critical thinking, or creativity. In this paper, we introduce an agentic AI chatbot architecture assisting students with exercise solving, specifically designed to contribute to more responsible AI use in education. We base our conceptual development on the identification of several desiderata for responsible LLM-based educational systems, argue for the structural shortcomings inherent in monolithic, out-of-the-box solutions, and instead suggest modularizing the agentic architecture. We propose specific modules for different stages of exercise solving, enabling incorporation of targeted pedagogical advice, guiding students through the learning process in a more controllable, transparent, and overseeable manner.
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