Understanding and Mitigating the Impacts of Code Intelligence Systems in Introductory Programming Courses

该项目旨在通过了解使用人工智能(AI)生成计算机代码对计算机科学教育的影响来服务于国家利益。人工智能工具可以从用自然语言编写的问题描述中生成计算机代码，它可能会导致更快、更容易地开发高质量的软件程序。虽然这些“代码智能”(CI)系统可能会积极地改善行业中专业开发人员的工作流程，但它们也有可能影响学生学习计算的方式。本研究将探讨使用CI系统分析学生的修修补补行为和自我效能感的影响，前者与学习收获有关，后者影响成绩。这些系统可能会帮助学习困难的学生，或者如果学生变得过于依赖它们，它们可能会缩短学生的学习时间。CI系统对教师在设计新的课程作业或识别学习环境中的抄袭行为方面构成了严重的挑战。在这项由BCSER项目支持的研究中，PI将熟练掌握学习理论和研究方法，以研究在大学编程入门课程中采用CI系统对学习的影响。这项研究的结果将有利于学生在计算机科学教育中的留存和多样化。本项目将利用学习科学知识的扩展概念来评估和表征代码生成工具对学生在编程入门课程中学习的影响。第一个项目的目标是开发必要的基础设施，以便在学生的网络开发环境中使用智能代码生成，使学生能够获得代码生成能力。第二个项目的目标是通过参与试点实验来了解代码生成工具在计算机科学入门课程中的使用，检查对修补和自我效能的影响。第三个项目目标是设计和评估干预措施，通过鼓励学生更密切地关注生成的代码来减轻对学生学习的潜在负面影响。这些将为积极使用传播与信息系统创建一个扩展的知识库。该项目的第四个主要目标包括参加与计算机科学教育研究有关的专业发展讲习班和在线课程，以及与导师举行形成性评估会议。这些将改善国际和平研究所和该机构在计算机科学教育研究方面的能力建设和可持续性。计算机科学研究界将通过出版物和在计算机科学会议上的介绍，获得经验教训、面临的挑战和开发的产品，包括相应的数据集和可再现性成套资料。该项目由NSF的STEM教育研究中的EHR核心研究能力建设计划(ECR：BCSER)支持，该计划旨在建设研究人员开展高质量STEM教育研究的能力。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。

This project aims to serve the national interest by understanding the impact on computer science education of using artificial intelligence (AI) to generate computer code. Artificial intelligence tools can generate computer code from the description of a problem written in natural language, and it may lead to faster and easier development of high-quality software programs. While these “code intelligence” (CI) systems may positively improve workflow for professional developers in industry, they also have potential to impact how students learn computing. This research will investigate the impacts of the use of CI systems to analyze students’ tinkering behaviors, which are associated with learning gains, and students’ feelings of self-efficacy, which affect performance outcomes. These systems may help struggling students learn, or they may shortcut student learning if the students become too reliant on them. CI systems can pose serious challenges for instructors in designing novel course assignments or identifying plagiarism in the learning environment. In this research supported by the BCSER program, the PI will develop proficiency in learning theories and research methods to study the impacts of the adoption of CI systems on learning in introductory college programming courses. The outcomes of this research will benefit student retention and diversity in computer science education.This project will utilize extended concepts from learning science knowledge to evaluate and characterize the impacts of code generation tools on students' learning in introductory programming courses. The first project goal is to develop the necessary infrastructure to use intelligent code generation within a web development environment for students, so that students have access to code-generating capabilities. The second project goal is to understand the use of the code generation tool in introductory computer science courses through engagement in pilot experiments examining effects on tinkering and self-efficacy. A third project goal is to design and evaluate an intervention to mitigate the potential negative impacts on students’ learning by encouraging students to pay closer attention to the generated code. These will create an extended knowledge base for positive use of CI systems. A fourth key goal of this project involves engagement in professional development workshops and online classes related to computer science education research as well as holding formative assessment meetings with mentors. These will improve capacity building and sustainability of computer science education research for the PI and the institution. The computer science research community will gain access to lessons learned, challenges faced and products developed, including corresponding datasets and reproducibility packages, through publications and presentations at computer science conferences. The project is supported by NSF's EHR Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators’ capacity to carry out high-quality STEM education research.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.