Transforming Introductory Computer Science Instruction with an AI-Driven Classroom Assistant

利用人工智能驱动的课堂助手改变计算机科学入门教学

• 批准号: 2236195
• 负责人: Bita Akram
• 金额: $ 172.35万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236195&HistoricalAwards=false
• 关键词: Transforming; Introductory; Computer; Science; Instruction

This project aims to serve the national interest by using artificial intelligence (AI) technology to transform introductory programming classrooms into active, engaging, and supportive learning environments for a diverse range of students. Advances in AI are rapidly transforming every aspect of human life, including education. The collaborative project team plans to develop INSIGHT, an AI-driven classroom assistant for introductory computer science that provides real-time support to both students and instructors. The INSIGHT classroom assistant has the potential to improve introductory computer science education by supporting students with adaptive feedback and supporting instructors with aggregate student coding analytics. The proposed project has the potential to help instructors to dynamically adapt their instruction to students' conceptual understanding of computer science concepts.This project has three main objectives. First is to design, develop, and iteratively refine the INSIGHT AI-driven classroom assistant. Second is to develop a deep understanding of how students learn computer science with AI-driven classroom assistants. Third is to design a set of effective instructional support principles for coding-enriched classroom interactions. The project team will collaborate with instructors in a wide range of introductory computer science courses at large and small public universities, and Historically Black Colleges and Universities. The team plans to evaluate the impacts of INSIGHT on improving students’ learning and engagement. Results from the project will be shared with practitioners and researchers through workshops and presentations at conferences on computer science and engineering education. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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.

该项目旨在通过使用人工智能（AI）技术将入门编程教室转变为各种学生的积极，参与和支持性学习环境来服务于国家利益。人工智能的进步正在迅速改变人类生活的方方面面，包括教育。合作项目团队计划开发INSIGHT，这是一种人工智能驱动的课堂助手，用于介绍计算机科学，为学生和教师提供实时支持。INSIGHT课堂助手有可能通过支持学生自适应反馈和支持教师聚合学生编码分析来改善入门计算机科学教育。建议的项目有可能帮助教师动态地调整他们的教学，以适应学生的概念理解的计算机科学概念。首先是设计、开发和迭代完善INSIGHT AI驱动的课堂助手。其次是深入了解学生如何通过人工智能驱动的课堂助手学习计算机科学。第三，设计一套有效的编码强化课堂互动的教学支持原则。该项目团队将与教师合作，在大型和小型公立大学以及历史上的黑人学院和大学开设广泛的计算机科学入门课程。该小组计划评估INSIGHT对改善学生学习和参与的影响。该项目的成果将通过计算机科学和工程教育会议上的研讨会和演讲与从业人员和研究人员分享。NSF IUSE：EHR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。

项目成果

Use of Large Language Models for Extracting Knowledge Components in CS1 Programming Exercises

使用大型语言模型提取 CS1 编程练习中的知识组件

• DOI: 10.1145/3626253.3635592
• 发表时间: 2024
• 期刊: Special Interest Group on Computer Science Education bulletin
• 作者: Niousha, Rose;Hoq, Muntasir;Akram, Bita;Norouzi, Narges
• 通讯作者: Norouzi, Narges

Towards Attention-Based Automatic Misconception Identification in Introductory Programming Courses

在入门编程课程中实现基于注意力的自动误解识别

• DOI: 10.1145/3626253.3635575
• 发表时间: 2024
• 期刊: Special Interest Group on Computer Science Education bulletin
• 作者: Hoq, Muntasir;Vandenberg, Jessica;Mott, Bradford;Lester, James;Norouzi, Narges;Akram, Bita
• 通讯作者: Akram, Bita