Collaborative Research: EAGER: AI-Assisted Just-in-Time Scaffolding Framework for Exploring Modern Computer Design

合作研究：EAGER：用于探索现代计算机设计的人工智能辅助即时脚手架框架

• 批准号: 2327972
• 负责人: Md Rahman
• 金额: $ 10万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327972&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; AI; Assisted; Collaborative; Research; EAGER; AI; Assisted

There is a need to train skilled computer architects to design innovative computer hardware. Software-based simulation is the backbone of computer system design and development. Such tools are also widely used for teaching computer architecture concepts. Currently, the simulators used for educational purposes have a steep learning curve, are not interesting for beginners, and are error-prone. Therefore, these simulators are mostly used by experienced researchers. This project introduces a novel framework and technology called Scaffolded AI-driven Learning Simulation (SAILS). SAILS enables an interactive and supportive computer architecture learning platform and offers design exercises covering different learning modes and difficulty levels. In the development phase, SAILS will be used by instructors at the University of Kansas and Florida International University to teach introductory and advanced computer architecture courses to about 400 undergraduate and graduate computer science and engineering students every year. Once SAILS is fully developed, it will serve as a framework to teach computer architecture in several US institutions.SAILS implements a novel AI-driven paradigm for reducing the learning curve of computer architecture simulators in educational settings. SAILS implements a front end that reduces the complexity of simulating simple to advanced systems for students with various backgrounds. SAILS back-end seamlessly connects to a state-of-the-art computer architecture simulator and provides just-in-time personalized assistance to the users. The assistance is provided by a centralized AI model trained by individual users’ and team data and global users’ experience with the framework. SAILS integrates the faded scaffolding approach to provide appropriate levels of support to individual learners and teams to maximize their learning. SAILS’s easy-to-use graphical user interface, engaging learning activities, and personalized scaffolding support a broad and diverse student population, including female and underrepresented minority students, in the computer architecture and design field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

有必要培训熟练的计算机架构师来设计创新的计算机硬件。基于软件的仿真是计算机系统设计和开发的骨干。此类工具也被广泛用于教授计算机体系结构概念。当前，用于教育目的的模拟器具有钢制学习曲线，对于初学者来说并不有趣，并且容易出错。因此，这些模拟器主要由经验丰富的研究人员使用。该项目介绍了一个新颖的框架和技术，称为脚手架AI驱动的学习模拟（SAILS）。帆可实现一个交互式和支持的计算机架构学习平台，并提供涵盖不同学习模式和困难水平的设计练习。在开发阶段，堪萨斯大学和佛罗里达国际大学的讲师将使用帆，每年向约400名本科和研究生计算机科学和工程专业的学生教授简介和高级计算机建筑课程。一旦帆充分开发，它将作为在美国几个机构中教授计算机架构的框架。Sails实现了一种新颖的AI驱动范式，用于减少教育环境中计算机架构模拟器的学习曲线。 Sails实施了前端，该前端减少了为具有不同背景的学生模拟简单的高级系统的复杂性。帆的后端无缝连接到最先进的计算机架构模拟器，并为用户提供正式的个性化帮助。该协助由一个集中的AI模型提供，该模型由个人用户，团队数据以及全球用户在框架方面的经验进行培训。帆集成了褪色的脚手架方法，以向个人学习者和团队提供适当水平的支持，以最大程度地提高他们的学习。 Sails在计算机架构和设计领域中的易于使用的图形用户界面，引人入胜的学习活动以及个性化的脚手架支持，包括女性和人数不足的少数族裔学生，包括女性和人数不足的少数族裔学生。这一奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子的优点和广泛的范围来评估的。