SortingHat: 用定制的数字教学助手重新定义操作系统教育
SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant
AI总结：
针对操作系统课程教学挑战，提出结合检索增强生成和多智能体强化学习的3D数字人教学助手SortingHat，提供个性化指导、自适应内容生成和自动评估。
摘要：
操作系统课程是计算机科学教育中最具挑战性的课程之一，原因在于其内部结构的复杂性和运行环境的多样性。传统的教学方法往往无法应对学生多样化的背景、学习速度和实际需求。为了应对这些挑战，我们提出了SortingHat，一个专为操作系统教育定制的个性化数字教学助手。SortingHat集成了先进的人工智能技术，包括检索增强生成框架和多智能体强化学习，以提供自适应、可扩展且有效的教育支持。SortingHat采用由大型语言模型驱动的3D数字人界面，提供个性化、富有同理心和上下文感知的指导。它根据每个学生的学习历史和学业表现生成定制的练习，强化薄弱环节并挑战高级概念。此外，该系统包含一个强大的评估流程，确保对学生提交的内容进行公平、一致和无偏见的评分，同时提供个性化的、可操作的改进反馈。通过结合个性化指导、自适应内容创建和自动评估，SortingHat将操作系统教育转变为一种引人入胜、沉浸式且可扩展的体验。
英文摘要：
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.
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。
