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.

第四工业革命（4IR）系统的复杂性日益增加，使它们非常容易受到网络攻击的影响，从而证明了4IR劳动力的网络安全培训。尽管在线计划非常可扩展，并且非常适合重新锻炼和提高现有劳动力，但目前，它们不适合4IR网络安全培训，需要访问专用硬件。此外，来自不同背景的学生具有不同的学习方式，具体取决于他们的成长，教育背景和动机。对于来自边缘化，代表性不足的社区的学生来说，成功的计划完成尤其具有挑战性，因为历史上缺乏高质量的教育，他们的成长年代（预/中/高中）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AI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，该框架通过视频讲座和虚拟现实（VR）的网络安全实验室培训了4IR安全劳动力，该框架是通过生成AI，使用情感分析的学生来评估学生的知识和知识的知识，从而对学生进行了个性化学习，从而对学生进行了个性化学习。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与教育研究人员之间的合作。研究团队将探索：1）一名基于AI的生成性互动讲师，以协助和指导学生通过其在线课程； 2）基于虚拟现实的网络安全培训实验室，该实验室允许游戏进行4IR网络安全培训，包括在极端情况下进行培训，而无需冒险昂贵的设备或人类生命； 3）基于变压器的机器学习方法来衡量学习活动期间的学生情感和知识理解； 4）基于AI的生成方法，使用学生情感和理解，结合Bloom的分类法，根据学生的学习需求个性化课程工作； 5）一种衡量学生在为边缘化学生服务时学习干预措施的有效性的非规范结果的方法； 6）一种方法来提高学生的归属感，学术成就和工程认同感，这​​将导致通过学位提高学术持久性。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点评估来获得支持，并具有更广泛的影响标准。