用AI助手个性化辅导操作系统学习，提升教学效率与体验
SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant
- 基于RAG与多智能体强化学习构建自适应教学系统
- 生成定制练习并自动批改，反馈精准且无偏见
- 3D数字人界面增强互动感，适合不同基础学生
操作系统课程因内部结构复杂、运行环境多样，是计算机教育中最具挑战性的课程之一。传统教学难以满足学生背景、学习速度和实践需求的差异。为此，我们提出SortingHat——一款专为操作系统教育设计的个性化数字助教。该系统融合检索增强生成（RAG）与多智能体强化学习（MARL），通过大语言模型驱动的3D数字人界面，提供个性化的共情式、上下文感知指导。系统根据学生学习历史与成绩生成定制练习，强化薄弱环节并拓展高阶概念。同时，内置评估流水线实现公平、一致、无偏的作业评分，并提供可操作的改进建议。通过个性化引导、自适应内容生成与自动化评估，SortingHat将操作系统教育转变为沉浸式、可扩展的学习体验。
原文摘要 · Abstract (English)
Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diversity of running environments. Traditional teaching methods often fail to address the diverse backgrounds, learning speeds, and practical needs of students. To tackle these challenges, we present SortingHat, a personalized digital teaching assistant tailored specifically for OS education. SortingHat integrates advanced AI technologies, including a retrieval augmented generation (RAG) framework and multi agent reinforcement learning (MARL), to deliver adaptive, scalable, and effective educational support. SortingHat features a 3D digital human interface powered by large language models (LLMs) to provide personalized, empathetic, and context aware guidance. It generates tailored exercises based on each student's learning history and academic performance, reinforcing weak areas and challenging advanced concepts. Additionally, the system incorporates a robust evaluation pipeline that ensures fair, consistent, and unbiased grading of student submissions while delivering personalized, actionable feedback for improvement. By combining personalized guidance, adaptive content creation, and automated assessment, SortingHat transforms OS education into an engaging, immersive, and scalable experience.
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