Designing Chatbot-Based Learning Experiences to Enhance Preservice Elementary Teachers’ Responsiveness to Student Mathematical Thinking

该项目旨在通过提高职前小学教师在数学方面的反应能力来服务于国家利益，从而改善学生的STEM学习。具体地说，肯纳索州立大学和密苏里大学哥伦比亚分校的首席研究人员将合作，通过使用基于人工智能(AI)的聊天机器人，在他们的职前教师中开发和培养响应性的数学教学技能。在学生的早期阶段打下坚实的数学基础对STEM劳动力的发展至关重要。响应式教学促进了学生的数学推理能力和对数学的积极态度。然而，许多职前教师在与真正的学生合作之前，练习响应性教学的机会有限。在这个项目中，研究人员将探索聊天机器人如何作为一个创新的学习平台，提供广泛的机会来练习和完善响应性教学技能，如注意、启发和探索学生的思维。该项目的成果将推进知识基础，扩大数学教师培训的最佳实践。利用基于设计的研究原则，该项目的目标是在分数教学的背景下开发、制定、评估和完善三个模块，分数是早期数学学习中的一个关键概念。在每个模块中，用户将：1)被呈现给教学实践的表示，2)参与实践的分解，3)参与与聊天机器人的真实交互，该聊天机器人既是分享特定数学策略的虚拟学生，也是支持交互的指导教师，以及4)反思他们的交互。该项目的活动范围包括：1)开发教学模块，包括基于人工智能的聊天机器人；2)传播这些模块，以产生更广泛的影响。该过程需要通过三次迭代来开发、制定、评估和改进模块。将收集各种数据来改进聊天机器人并评估它们的效能。这将产生可用于其他数学主题和STEM领域的教师教育的设计原则。NSF IUSE：EDU计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过其参与的学生学习跟踪，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 improving preservice elementary teachers’ responsive teaching skills in mathematics, leading to improved STEM learning of their students. Specifically, principal investigators at Kennesaw State University and the University of Missouri-Columbia, will collaborate to develop and nurture responsive teaching skills in mathematics among their preservice teachers by using Artificial Intelligence (AI)-based chatbots. Laying a solid mathematical foundation in students’ early years is essential to the development of a STEM workforce. Responsive teaching promotes students’ mathematical reasoning abilities and positive attitudes toward mathematics. However, many preservice teachers have limited opportunities to practice responsive teaching prior to working with real students. In this project, researchers will explore how chatbots can serve as an innovative learning platform to provide extensive opportunities to practice and refine responsive teaching skills such as noticing, eliciting, and exploring student thinking. The outcomes of the project will advance the knowledge base and expand best practices for teacher preparation in mathematics.Using principles of design-based research, the project’s goals are to develop, enact, evaluate, and refine three modules in the context of teaching fractions, a critical concept in early mathematics learning. In each module, users will: 1) be presented with a representation of teaching practice, 2) engage in a decomposition of practice, 3) participate in authentic interaction with a chatbot that serves as both a virtual student sharing a particular mathematical strategy and a mentor teacher supporting the interaction, and 4) reflect on their interactions. The scope of the project's activities includes: 1) developing the instructional modules including AI-based chatbots and 2) disseminating the modules for broader impact. The process entails developing, enacting, evaluating, and refining the modules through three iterations. Various data will be collected to improve the chatbots and evaluate their efficacy. This will generate design principles that can be used in teacher education across other mathematical topics and STEM fields. 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.