用大模型生成可解释的智能解题代理，提升教育AI的可控性
Cognitive Agent Compilation for Explicit Problem Solver Modeling
- 将大模型知识编译为显式结构化解题代理
- 实现知识状态可查看、策略可编辑、推理可验证
- 适合需要透明教学逻辑的教育系统开发者
大型语言模型在辅导、反馈生成和内容创作中广泛应用，但其广泛预训练导致难以控制，难以替代可调控的学习者。教育系统需要可检查、可编辑的知识状态：教师需了解系统对学习者掌握情况的假设，学习者则受益于系统能以明确技能、误解和策略解释行为。受认知架构启发，我们提出认知代理编译（CAC）框架，利用强教师模型将问题求解知识编译为显式目标代理。CAC分离知识表示、求解策略与验证更新规则，旨在使受限问题求解在教育场景中更具可检查性和可编辑性。我们通过小型语言模型实现早期概念验证，揭示了显式控制与可扩展泛化之间的关键权衡，并将CAC定位为面向教育应用的有界知识AI的初步探索。
原文摘要 · Abstract (English)
Large language models (LLMs) are widely used for tutoring, feedback generation, and content creation, but their broad pretraining makes them hard to constrain and poor substitutes for controllable learners. Educational systems often require inspectable and editable knowledge states: educators want to know what a system assumes the learner knows, and learners benefit when the system can justify actions in terms of explicit skills, misconceptions, and strategies. Inspired by cognitive architectures, we propose Cognitive Agent Compilation (CAC), a framework that uses a strong teacher LLM to compile problem-solving knowledge into an explicit target agent. CAC separates (i) knowledge representation, (ii) problem-solving policy, and (iii) verification and update rules, with the goal of making bounded problem solving more inspectable and editable in educational settings. We present an early proof of concept implemented with Small Language Models that surfaces key design trade-offs, particularly between explicit control and scalable generalization, and positions CAC as an initial step toward bounded-knowledge AI for educational applications.
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