Education DCL: EAGER: Generative AI-based Personalized Cybersecurity Tutor for Fourth Industrial Revolution

教育 DCL：EAGER：第四次工业革命的基于生成人工智能的个性化网络安全导师

• 批准号: 2335046
• 负责人: Pratik Satam
• 金额: $ 30万
• 依托单位: University of Arizona
• 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: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335046&HistoricalAwards=false
• 关键词: Education; DCL; EAGER; Generative; AI

The increasing complexity of Fourth Industrial Revolution (4IR) systems makes them highly vulnerable to cyberattacks, justifying cybersecurity training for the 4IR workforce. Although online programs are highly scalable and ideal for reskilling and upskilling an existing workforce, currently, they are unsuitable for 4IR cybersecurity training which requires access to specialized hardware. In addition, students from different backgrounds have different learning styles depending on their upbringing, educational backgrounds, and motivations. Successful program completion is especially challenging for students from marginalized, underrepresented communities, as a historical lack of access to high-quality education during their formative years (pre/middle/high schools) can impede their success. To address these challenges, this project leverages a Generative AI based Personalized Cybersecurity Tutor (gAI-PCT), a scalable online learning framework that trains a 4IR security workforce through video lectures and virtual reality (VR) based cybersecurity labs, personalized to the students learning needs through generative AI, using sentiment analysis to evaluate student interest and knowledge comprehension. This research is a collaboration between cybersecurity and education researchers at the University of Arizona and Sandia National Labs.The research team will explore: 1) a Generative AI-based interactive instructor to assist and guide students through their online coursework; 2) Virtual Reality based cybersecurity training labs that allow gamified 4IR cybersecurity training, including training on extreme scenarios without risking expensive equipment or human life; 3) Transformer-based machine learning approach to gauge student sentiments and knowledge comprehension during learning activities; 4) Generative AI-based approach to use student sentiment, and comprehension, in combination with Bloom's Taxonomy, to personalize coursework according to student's learning needs; 5) An approach to gauge non-normative outcomes measuring the effectiveness of students learning interventions while serving marginalized students; 6) An approach to increasing student's sense of belonging, academic attainment, and engineering identity that will result in increased academic persistence through the degrees.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.

第四次工业革命 (4IR) 系统日益复杂，使其极易受到网络攻击，因此对 4IR 员工进行网络安全培训是合理的。尽管在线课程具有高度可扩展性，非常适合对现有员工进行再培训和提升技能，但目前它们不适合需要使用专用硬件的 4IR 网络安全培训。此外，来自不同背景的学生因其成长、教育背景和动机而有不同的学习风格。对于来自边缘化、代表性不足社区的学生来说，成功完成课程尤其具有挑战性，因为历史上在他们的成长时期（学前/初中/高中）缺乏获得高质量教育的机会可能会阻碍他们的成功。为了应对这些挑战，该项目利用基于生成式人工智能的个性化网络安全导师 (gAI-PCT)，这是一个可扩展的在线学习框架，通过视频讲座和基于虚拟现实 (VR) 的网络安全实验室来培训 4IR 安全人员，并针对学生的学习进行个性化通过生成人工智能满足需求，使用情感分析来评估学生的兴趣和知识理解程度。这项研究是亚利桑那大学和桑迪亚国家实验室的网络安全和教育研究人员之间的合作。研究团队将探索：1）基于生成式人工智能的交互式讲师，帮助和指导学生完成在线课程； 2) 基于虚拟现实的网络安全培训实验室，允许进行游戏化的 4IR 网络安全培训，包括在极端场景下进行培训，而不会冒昂贵设备或人员生命的风险； 3）基于Transformer的机器学习方法来衡量学生在学习活动中的情绪和知识理解； 4) 基于生成式人工智能的方法，利用学生的情感和理解力，结合布鲁姆分类法，根据学生的学习需求个性化课程作业； 5) 一种衡量非规范结果的方法，衡量学生学习干预措施的有效性，同时为边缘化学生提供服务； 6) 一种提高学生归属感、学术成就和工程认同感的方法，从而通过学位提高学术毅力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的评估进行评估，被认为值得支持影响审查标准。