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

合作研究：EAGER：SaTC-EDU：在人工智能生成的超现实数据时代保护 STEM 教育和科学知识

• 批准号: 2039613
• 负责人: Conrad Tucker
• 金额: $ 10万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039613&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; SaTC; EDU

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.

人工智能（AI）系统的出现可以创建超现实的数据（例如，人脸图像或网络流量数据），这对试图确定真假的人和计算机都提出了挑战。这些进步对STEM学习者和网络安全网络来说既是威胁，也是机遇。一方面，人工智能生成超现实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，如果没有强大的网络安全保障，人工智能生成的数据有可能降低在线公开获取的知识的准确性。该项目建议进行一系列研究，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理解STEM教育材料与网络安全网络之间的相互联系，以及它们在面对超现实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 EAGER项目汇集了K-12（挑战者中心）、高等教育（卡内基梅隆大学）和劳动力（兰德公司）的研究人员，调查在超现实人工智能生成数据的时代，STEM教育材料和计算机网络流量数据的自由流动所带来的风险。参与研究的参与者将被随机展示假的STEM内容（即，由生成神经网络生成的STEM内容，并被修改为包含错误信息）与在STEM信息交流中真实的STEM内容。每个参与者将被要求对显示的STEM内容是假的还是真实的进行分类。额外的问题将通过随机分配包含或省略这些特征的STEM内容的参与者版本来探索向参与者展示的STEM内容的特定特征如何作为真实性指标。考虑到不同学习者群体的年龄和经验水平，对不同学习者群体（K-12、高等教育和成年劳动力）的研究将阐明学习者从人工智能改变的STEM教育材料中解读事实教育材料的能力之间存在的可变性。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。