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、计算机辅助医学成像、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，导致对物体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，因此，重要的是要教学生分析基于人工智能的系统中的漏洞所需的技能，以及这些系统如何可能失败，以及如何缓解这些问题，以帮助创建更值得信赖的基于人工智能的系统。 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，其目标是发展课程，吸引并吸引多样化的学生群体。在网络安全和人工智能的交叉点上工作的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由于不平衡的数据集或仅关注可用数据集整体准确性的训练方法而存在隐性漏洞和盲点。项目团队建议在网络安全和人工智能的交叉点教授和研究三门课程，包括创建一门关于可信人工智能的新课程。课程将解决的主题，这将促使学生考虑如何人口的部分可能会在身份验证，隐私和用户安全等领域受到不同的影响。学习科学和教育心理学方法（特别是焦点小组和临床访谈）将用于确定学习和教学的挑战，并描述概念和误解。该项目将产生五个可交付成果：网络安全和人工智能十字路口的示范课程;管理此类课程中跨学科性的策略;学生概念的特征;确定学生学习挑战;以及确定网络安全和人工智能的新研究方向。调查结果和课程构想将广泛传播。该项目得到了安全和值得信赖的网络空间（SaTC）计划的特别倡议的支持，以促进网络安全，人工智能和教育领域之间新的，以前未探索的合作。SATC计划与联邦网络安全研究和发展战略计划和国家隐私研究战略保持一致，以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NSF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.