CyberTraining: Implementation: Small: Promoting AI Readiness for Machine-Assisted Secure Data Analysis (PAIR4MASDA)

人工智能（AI）技术的民主化，如大型语言模型和聊天机器人，加速了对人工智能就绪劳动力的需求。为了应对这一挑战，该项目旨在向广泛的高级网络基础设施（CI）用户和研究人员灌输人工智能准备，以便他们能够高效、和谐地使用人工智能进行安全的大数据分析。该项目直接涉及18名科学和工程教师，10名行业专家，以及来自合作大学和学院的1000多名学生，包括几所少数民族服务机构。通过提供可获取和相关的学习材料和全面的专业发展，促进不同人群对人工智能的准备，该项目旨在通过自下而上的赋权，为那些感到落后的人创造公平的竞争环境。集体影响引导团队共同努力，扩大妇女、少数民族和退伍军人等代表性不足群体的参与。所有的培训材料在设计上都具有可重复性，以方便广泛的学习者采用，包括那些目前没有上大学或无法获得计算或传统教育资源的学习者。先进的网络基础设施（CI）支持具有大数据分析的人工智能（AI）是科学和工程（S&amp；E）研究的重要推动力。然而，正如最近涉及聊天机器人的事故所表明的那样，人工智能可能会产生令人信服但不正确甚至有害的结果。人工智能还不能完全自主，尤其是在关键任务应用中。强调机器辅助的安全数据分析，与人在循环中提高安全性，安全性和可靠性。为了响应对人工智能培训的多方面方法的反复呼吁，该项目从技术、心理和行为三个方面研究人工智能准备情况。期望确认理论扩展了决策质量，并与普适技术策略相结合。这一发展既促进了知识的发展，又为精确和可测量的结果提供了理论基础。一套全面的体验式学习模块构成了一套可定制培训材料的核心。关键的知识和技能被提炼成被称为灵活微模块（fmm）的小单元，以快速提高学习者的技能，他们反过来创建自己的个性化工具包。另外一个层建立在顶部，使用扩展现实（XR）技术为学习者提供多模态沉浸式体验。这些身临其境的体验有助于学习者更好地理解困难的概念，并内化复杂的任务序列。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由美国国家科学基金会STEM教育理事会（EDU）的研究生教育处（DGE）和计算机与信息科学与工程理事会（CISE）的信息与智能系统（IIS）部门共同支持。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。

Democratization of artificial intelligence (AI) technologies, such as large language models and chatbots, has accelerated the need for an AI ready workforce. To meet this challenge, the project aims to instill AI readiness in a broad spectrum of users and researchers of advanced cyberinfrastructure (CI), so they can productively and harmoniously use AI for secure big data analysis. The project directly involves eighteen science and engineering faculty members, ten industry experts, and over a thousand students across partner universities and colleges, including several minority serving institutions. By promoting AI readiness across diverse populations with accessible and relatable learning materials and holistic professional development, the project aims to level the playing field for those who feel left behind via bottom-up empowerment. Collective impact guides the team's concerted effort to broaden participation from underrepresented groups such as women, minorities, and veterans. All training materials have reproducibility built in by design to facilitate adoption by a broad range of learners, including those not currently attending college or otherwise having limited access to computational or traditional educational resources.Advanced cyberinfrastructure (CI) enabled artificial intelligence (AI) with big data analysis is an important enabler for science and engineering (S&E) research. However, as recent mishaps involving chatbots show, AI can produce convincing but incorrect or even harmful results. AI cannot yet be trusted with full autonomy, especially in mission critical applications. There is an emphasis on machine assisted secure data analysis, with human in the loop to enhance safety, security, and reliability. Responding to repeated calls for a multi-faceted approach to AI training, the project studies AI readiness along three dimensions: technical, psychological, and behavioral. Expectation confirmation theory is extended with decision quality and coupled with pervasive technology strategies. This development both advances knowledge and provides a theoretical basis for precise and measurable outcomes. A comprehensive suite of experiential learning modules forms the core of a set of customizable training materials. Critical knowledge and skills are distilled into bitesize units known as flexible micro modules (FMMs) to rapidly upskill learners, who in turn create their own personalized toolkits. An additional layer is built on top to give learners multimodal immersive experiences using extended reality (XR) technologies. These immersive experiences help learners better comprehend difficult concepts and internalize complex sequences of tasks. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Graduate Education (DGE) within the NSF Directorate for STEM Education (EDU) and Information and Intelligent Systems (IIS) division within the Computer and Information Science and Engineering (CISE) directorate.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.