EAGER: SaTC-EDU: Identifying Educational Conceptions and Challenges in Cybersecurity and Artificial Intelligence

人工智能（AI）在许多数据密集型新兴领域有着重要的应用，如自动驾驶汽车、计算机辅助医疗成像、行为分析、用户身份验证、网络安全和智能基础设施的嵌入式系统。然而，关于对人工智能系统的信任，还有一些悬而未决的问题。越来越多的证据表明，机器学习算法可能被恶意操纵，导致对象和语音的错误分类和错误检测。随着基于人工智能的技术越来越多地被采用，教授学生分析基于人工智能的系统中的漏洞以及这些系统如何可能失败所需的技能，以及如何减轻这些问题以帮助创建更值得信赖的基于人工智能的系统，这一点非常重要。该项目汇集了来自教育、人工智能和网络安全领域的专家，以确定值得信赖的人工智能教学主题的挑战和潜在解决方案，目标是不断发展课程，吸引和吸引多样化的学生群体。在网络安全和人工智能的交叉点实现劳动力多样化至关重要，因为基于人工智能的系统可能容易出现隐性漏洞和盲点，这是由于数据集不平衡或训练方法只关注可用数据集的整体准确性。项目团队拟在网络安全和人工智能交叉领域开设三门课程，包括开设一门关于可信赖人工智能的新课程。课程作业将讨论一些主题，这些主题将促使学生考虑如何在身份验证、隐私和用户安全等领域对人群产生不同的影响。学习科学和教育心理学方法（特别是焦点小组和临床访谈）将用于识别学习和教学挑战，并表征概念和误解。该项目将产生五项成果：网络安全和人工智能交叉的示范课程；管理此类课程中的跨学科性的策略；学生概念的特征；识别学生的学习挑战；确定网络安全和人工智能的新研究方向。调查结果和课程理念将广泛传播。该项目由安全与可信网络空间（SaTC）计划的一项特别倡议支持，旨在促进网络安全、人工智能和教育领域之间前所未有的合作。SaTC项目与《联邦网络安全研究与发展战略计划》和《国家隐私研究战略》保持一致，旨在保护和维护网络系统日益增长的社会和经济效益，同时确保安全和隐私。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Artificial intelligence (AI) has significant applications to many data-intensive emerging domains such as automated vehicles, computer-assisted medical imaging, behavior analysis, user authentication, cybersecurity, and embedded systems for smart infrastructures. However, there are unanswered questions relating to trust in AI systems. There is increasing evidence that machine learning algorithms can be maliciously manipulated to cause misclassification and false detection of objects and speech. With the growing adoption of AI-based techniques, it is therefore important to teach students the skills needed to analyze vulnerabilities in AI-based systems and how such systems may fail, as well as how to mitigate such issues to help create more trustworthy AI-based systems. This project brings together experts from the areas of education, AI, and cybersecurity to identify challenges and potential solutions to teaching topics in trustworthy AI with the goal of evolving coursework that will appeal to, and engage, a diverse student body. It is critical to diversify the workforce operating at the intersection of cybersecurity and AI because AI-based systems can be prone to implicit vulnerabilities and blind spots due to imbalanced datasets or training methods that focus only on the overall accuracy of available datasets. The project team proposes to teach and study three courses at the intersection of cybersecurity and AI, including creating a new course on trustworthy AI. Coursework will address topics that will spur students to consider how segments of the population may be differentially impacted in areas such as authentication, privacy, and user safety. Learning science and educational psychology approaches (specifically focus groups and clinical interviews) will be used to identify learning and teaching challenges and to characterize conceptions and misconceptions. The project will produce five deliverables: model curricula at the crossroads of cybersecurity and AI; strategies for managing cross-disciplinarity in such curricula; characterizations of student concepts; identification of student learning challenges; and identification of new research directions in cybersecurity and AI. The findings and curricular ideas will be disseminated broadly. This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.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.