Collaborative Research: EAGER: SaTC-EDU: Safeguarding STEM Education and Scientific Knowledge in the Age of Hyper-Realistic Data Generated Using Artificial Intelligence

能够创建超逼真数据(例如，人脸图像或网络流量数据)的人工智能(AI)系统的出现给试图确定什么是真的，什么是假的人和计算机都提出了挑战。这些进展对STEM学习者和网络安全网络既构成威胁，也带来机遇。一方面，人工智能生成超逼真数据的能力有可能增加学生对人工智能、STEM和网络安全的兴趣。另一方面，如果没有强有力的网络安全保障，人工智能生成的数据可能会降低在线公开获得的知识的真实性。该项目提议进行一系列研究，向学习者展示人工智能生成的STEM内容，并要求他们确定其真实性。该项目旨在发现不同人群(K-12、高等教育和成年劳动力)的脆弱性水平是否存在差异。该项目将为更深入地了解STEM教育材料和网络安全网络之间的互联关系，以及它们在面临超逼真人工智能生成数据的挑战时所面临的共性奠定基础。这个NSF ENGER项目汇集了来自K-12(Challenger Center)、高等教育(Carnegie Mellon University)和劳动力(RAND Corporation)的研究人员，调查在超逼真人工智能生成数据时代对STEM教育材料和计算机网络流量数据自由流动带来的风险。参与研究的参与者将被随机展示虚假的STEM内容(即，由生成神经网络生成的并已被修改为包括错误信息的STEM内容)与在其STEM信息交流中真实的STEM内容。每个参与者将被要求对正在展示的STEM内容进行分类，是假的还是真的。其他问题将探讨向参与者展示的STEM内容的具体特征如何通过随机分配给参与者包含或省略这些特征的STEM内容版本来作为真实性指标。对不同学习者群体(K-12、高等教育和成人劳动力)的研究将阐明在不同学习者群体的年龄和经验水平下，学习者从人工智能改变的STEM教育材料中破译事实教育材料的能力之间存在的差异。该项目得到了安全和值得信赖的网络空间(SATC)计划的一项特别倡议的支持，该计划旨在促进网络安全、人工智能和教育领域之间新的、以前从未探索过的合作。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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The emergence of artificial intelligence (AI) systems that can create hyper-realistic data (e.g., images of human faces or network traffic data) presents challenges both to people and computers trying to determine what is authentic and what is fake. These advances pose both a threat and an opportunity for STEM learners and cybersecurity networks. On one hand, the ability of AI to generate hyper-realistic data has the potential to increase students’ interest in AI, STEM, and cybersecurity. On the other hand, AI-generated data, without robust cybersecurity guarantees, have the potential to reduce the veracity of knowledge that is publicly available on-line. This project proposes to conduct a series of studies where learners are presented with AI-generated STEM content and asked to determine its authenticity. The project seeks to discover whether differences exist in the level of vulnerabilities across diverse populations (K-12, higher education, and the adult workforce). The project will lay the foundation for a deeper understanding of the interconnectedness between STEM education materials and cybersecurity networks, and the commonalities that they face when challenged with the presence of hyper-realistic AI-generated data. This NSF EAGER project brings together researchers from K-12 (Challenger Center), higher education (Carnegie Mellon University), and the workforce (RAND Corporation) to investigate risks posed to the free flow of STEM education materials and computer network traffic data in the age of hyper-realistic AI-generated data. Participants engaged in the study will be randomly shown fake STEM content (i.e., STEM content that is generated by Generative Neural Networks and has been modified to include misinformation) vs. STEM content that is authentic in its communication of STEM information . Each participant will be asked to classify whether the STEM content being displayed is fake or authentic. Additional questions will probe how specific characteristics of the STEM content displayed to participants serve as indicators of authenticity by randomly assigning participants versions of the STEM content that contain or omit those characteristics. The study of different learner populations (K-12, higher education, and the adult workforce) will elucidate the variability that exists amongst learners’ ability to decipher factual education material from AI-altered STEM education material, given the age and experience level of different learner populations. 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.