Artificial Intelligence-Scaffolded Pre-Classroom Learning for Large, Introductory Undergraduate Physics Courses

该项目旨在通过设计和实施人工智能（AI）增强的形成性评估和反馈系统，为国家利益服务。该系统将帮助学生发展基于源的STEM论证，如STEM文本摘要或问题空间，这是问题的心理表征和解决问题的多种途径。项目的实施将在一所城市大学的大型本科物理导论课程中进行，该大学为多样化和历史上代表性不足的学生群体提供服务。学习者持续参与课前学习活动对于学习者在STEM入门课程中取得成功至关重要。本科生通常需要在自定进度的在线学习环境中培养对内容或问题情境的深刻理解，为课堂上的主动和协作学习做好准备。然而，在以学生为中心的学习模式中，无监督的课前学习可能是一个持续存在的问题。这种问题在大型入门级STEM课程中尤为明显，传统的教学方法在这些课程中效果不佳。该项目的创新将包括人工智能生成的自适应脚手架信息和使用数据可视化技术的学习进度反馈，以帮助学生进行概念性学习和自我监管行为。由人工智能支撑的反馈系统支持的独特学习机会将显着提高学生在自学在线课前学习中的参与度。反过来，这应该帮助学生获得内容知识，建立对问题的正确理解，为在课堂上成功地进行互动解决问题的活动做好准备。该项目的工作将分为三个阶段。首先，项目团队将采用参与式研究（PR）方法，强调教授物理课程的教师在设计和实施新作业时的直接参与。这些教师还将通过与研究人员合作，共同构建研究，对课程中的教师和学生进行混合方法研究。在第一阶段，主要研究目标是确定利用人工智能框架的课前学习的主题和问题，并调查学习者在课前作业中的参与度和进展情况。在项目的第二阶段，评估研究将证明课前学习中的知识发展是否能帮助学生解决课堂上的认知要求任务，并在STEM中培养积极的自我效能感。研究结果还将确定教育中的人工智能是否能改善学生在课堂内外的幸福感，重点关注传统上在STEM教育中代表性不足的学生。在最后阶段收集的大量数据将揭示课前活动、课堂表现、自我效能感、对物理的兴趣和学生背景（包括性别、种族、民族、第一代身份和英语语言学习）之间的关系。序列挖掘和聚类分析有望揭示学生不同的隐性参与状态，并对他们的参与轨迹进行分组，解释聚类隶属度和轨迹在不同学生背景下的差异。因此，该项目将为进一步研究奠定基础，以开发一种基于人工智能的课前学习模式，促进大多数学生在物理入门课程中取得成功。NSF IUSE: EDU项目支持研究和开发项目，以提高所有学生STEM教育的有效性。通过其参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。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。

This project aims to serve the national interest by designing and implementing an Artificial Intelligence (AI)-augmented formative assessment and feedback system. This system will help students develop source-based STEM arguments, such as STEM text summarization, or problem spaces, which are mental representations of a problem and of multiple paths to solving it. Project implementation will take place in large, undergraduate introductory physics courses at an urban university that serves diverse and historically underrepresented student groups. Persistent learner engagement in pre-classroom learning activities is critical to learner success in introductory STEM courses. Undergraduate students often need to develop a solid understanding of content or problem situations in self-paced online learning contexts to prepare for in-classroom active and collaborative learning. However, unsupervised pre-classroom learning can be an ongoing issue in a student-centered learning model. This problematic situation is particularly evident in large introductory-level STEM courses where traditional instructional techniques are less effective. The innovation of this project will include AI-generated adaptive scaffolding information and learning progress feedback with data visualization techniques to help students with conceptual learning and self-regulatory behaviors. The unique learning opportunities supported by an AI-scaffolded feedback system will significantly increase students' engagement levels in self-paced online pre-classroom learning. This, in turn, should help students acquire content knowledge and build a proper understanding of problems to prepare themselves for success with in-classroom interactive problem-solving activities.Three phases will govern the work of this project. First, the project team will take a Participatory Research (PR) approach that emphasizes the direct engagement of faculty members who teach physics courses in designing and implementing new assignments. These faculty members will also co-construct research through a partnership with researchers to conduct a mixed-methods study of instructors and students in the courses. During this first phase the primary research goal is to identify topics and problems that utilize AI-scaffolded pre-classroom learning and investigate learner engagement and progression in the pre-class assignments. In the project's second phase evaluation studies will demonstrate whether knowledge development during pre-classroom learning can help students solve cognitively demanding tasks in classrooms and develop positive self-efficacy in STEM. The findings will also determine whether AI in education improves students' well-being inside and outside of classrooms, with a focus on students traditionally underrepresented in STEM education. Extensive data collected in the final phase will uncover the relationships among pre-classroom activities, in-classroom performance, self-efficacy, interest in physics, and student backgrounds, including gender, race, ethnicity, first-generation status, and English language learning. The sequence mining and cluster analysis are expected to reveal students' different hidden engagement states and group their engagement trajectories, explaining how cluster membership and trajectories vary across students' backgrounds. Consequently, this project will lay the groundwork for further research to develop an AI-scaffolded pre-classroom learning model that promotes most students' success in introductory physics courses. The NSF IUSE: EDU 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.