Building AI-Powered Responsible Workforce by Integrating Large Language Models into Computer Science Curriculum

通过将大型语言模型集成到计算机科学课程中，打造人工智能驱动的负责任的劳动力队伍

• 批准号: 2336061
• 负责人: Dianxiang Xu
• 金额: $ 75万
• 依托单位: University of Missouri-Kansas City
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2336061&HistoricalAwards=false
• 关键词: Building; AI; Powered; Responsible; Workforce

This project aims to serve the national interest by integrating Large Language Models (LLMs) to improve curricula in computer science (CS) education. The field of software development is currently experiencing a profound transformation driven by significant advancements in LLMs. This shift demands that software developers acquire new skills to fully leverage the potential of LLMs. These skills encompass prompt engineering for software development, the management of structural complexity, debugging Artificial Intelligence (AI)-related errors, and adherence to ethical guidelines and principles. This engaged student learning level two project intends to harness the power of LLMs to elevate student engagement, promote active learning, and foster critical thinking within the CS program. This project has the potential to advance CS education, educate students on ethical and responsible AI practices, and promote responsible software development.Specifically, this project will: (a) utilize LLMs as potent educational tools to enhance students' problem-solving and programming proficiency in core programming courses; (b) integrate LLMs as software development tools within the software engineering course sequence; (c) address ethical and legal considerations related to LLMs. To achieve these objectives, the project will develop a comprehensive set of instructional materials to transform teaching methods and assess learning outcomes. Additionally, the project will host faculty development workshops to foster knowledge sharing and collaboration among educators, facilitating the effective adoption and integration of the proposed instructional resources. The project intends to share the results and findings of this project with a broader computer science education community. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.

该项目旨在通过整合大型语言模型（LLM）来改善计算机科学（CS）教育课程，从而为国家利益服务。软件开发领域目前正在经历一场深刻的变革，这是由LLM的重大进步推动的。这种转变要求软件开发人员获得新的技能，以充分利用LLM的潜力。这些技能包括软件开发的快速工程，结构复杂性的管理，调试人工智能（AI）相关的错误，以及遵守道德准则和原则。这个参与学生学习水平两个项目旨在利用LLM的力量，以提高学生的参与度，促进主动学习，并在CS程序中培养批判性思维。该项目将：（a）利用法学硕士课程作为有效的教育工具，提高学生在核心编程课程中解决问题和编程的能力;（B）将法学硕士课程作为软件开发工具纳入软件工程课程序列;（b）将法学硕士课程作为软件开发工具纳入软件工程课程序列。（c）处理与法学硕士课程有关的道德和法律的考虑。为了实现这些目标，该项目将开发一套全面的教学材料，以改革教学方法和评估学习成果。此外，该项目还将举办教师发展讲习班，以促进教育工作者之间的知识共享和合作，促进有效采用和整合拟议的教学资源。该项目旨在与更广泛的计算机科学教育社区分享该项目的成果和发现。NSF IUSE：EDU计划支持研究和开发项目，以提高所有学生STEM教育的有效性。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。