Exploring Artificial Intelligence-enhanced Electronic Design Process Logs: Empowering High School Engineering Teachers

工程设计过程（EDP）是一个通用的理论框架，通常用于工程，STEM，发明，甚至科学教学，特别是在K-12教育中。虽然大多数教育中使用的EDPs被描述为线性或循环，但真正的设计过程是高度创造性的，非线性的，并且经常涉及不适定的问题陈述和解决标准。 这些特点使得高中工程教师特别困难，他们倾向于跳过以人为本的设计的关键要素，工程师需要时间通过研究，访谈，先前的文献搜索，市场分析和头脑风暴来理解问题-思想和经验的多样性是最有价值的步骤。此外，由于小组工作的异步性质和大班规模，很难为学生提供实时反馈，学生可能会对未完成的工作要求反馈感到不舒服。该项目将开发和试点人工智能（AI）增强的工程设计过程日志，以帮助学生导航设计过程，提供实时反馈，并鼓励过程的每一步有意义的文档。 该项目并不打算用人工智能取代教师;相反，该项目将探索一种新的方法，让人工智能系统帮助教师创建符合EDP最佳实践的教学模块。该项目是格鲁吉亚理工学院计算学院（GT CoC）的研究人员与格鲁吉亚理工学院科学、数学和计算一体化教育中心（CEISMC）的研究人员之间的合作。 该项目是一项以教学为中心的技术创新，代表了对人工智能增强设计教学法的早期探索。具体而言，该项目将：1）通过参与教师用户研究来改进现有的基于Web的工程设计过程日志（EDPL），2）设计，试点和实施基于AI的创作和辅导系统，供教师为学生和具有领域专业知识的特定项目定制反馈，3）为alpha和beta测试教师设计和提供专业发展机会，以及4）评估基于AI的EDP日志（AI-EDPL）对工程设计教学法和课堂实践的影响。AI-EDPL软件系统将使用最初为智能辅导系统开创的概念，但应用于创建定制的教师制作的教学材料，这些材料遵循设计过程教学法评估的最佳实践。与许多其他教育领域不同，工程设计问题在范围和解决途径上差异很大，这意味着不会有一个一刀切的辅导系统可以为学生提供反馈。该项目将研究（a）人工智能是否可以支持和支架教师创建支架和支持学习者所需的必要模型和知识结构，以及（B）专业发展教师需要成功地开发这些模型。一个多阶段的方法将被使用，使用价值敏感的设计过程，从人机交互领域开发最低限度的功能系统，可以在教室里与教师进行测试。为了让人工智能帮助没有太多时间修补软件的教师，他们必须能够用自然语言表达自己的意图，这些意图必须自动转换为可以轻松编辑的任务模型的函数近似。该项目将建立在设计理论和教学法，设计文档，设计教学，设计评估和人工智能辅导的最佳实践基础上，以创建适合高中工程设计教学的独一无二的技术。它代表了在计算环境中为开放式设计挑战提供实时反馈的第一次尝试，并且它没有边缘化或减少讲师在工程课堂中的作用。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

The Engineering Design Process (EDP) is a general theoretical framework often used for teaching engineering, STEM, invention, and even science, particularly in K-12 education. While most EDPs used in education are depicted as linear or circular, true design processes are highly creative, non-linear, and often involve ill-posed problem statements and solution criteria. These traits make it particularly difficult for high school engineering teachers, who tend to skip over key elements of human-centered design, where an engineer takes time to understand the problem through research, interviews, prior literature searches, market analysis, and brainstorming—the steps where diversity of thought and experience are of the most value. In addition, it can be hard to provide students with real-time feedback due to the asynchronous nature of group work and large class sizes, and students may not feel comfortable asking for feedback on incomplete work. This project will develop and pilot an artificial intelligence (AI) enhanced Engineering Design Process Log to help students navigate the design process, provide real-time feedback, and encourage meaningful documentation of each step of the process. This project does not propose to replace teachers with AI; rather, the project will explore a novel approach in which AI systems assist teachers in the creation of instructional modules that adhere to EDP best practices. This project is a collaboration between researchers at Georgia Tech’s College of Computing (GT CoC) and researchers at Georgia Tech’s Center for Education Integrating Science, Mathematics and Computing (CEISMC). This project is a teaching-focused technological innovation, representing an early exploration into AI-enhanced design pedagogy. Specifically, the project will: 1) Improve upon an existing web-based Engineering Design Process Log (EDPL) by engaging in teacher user studies, 2) Design, pilot, and implement an AI-based authoring and tutoring system for teachers to customize feedback for students and for specific projects with domain expertise, 3) Design and provide professional development opportunities for alpha and beta testing teachers, and 4) Assess the impact of an AI-based EDP Log (AI-EDPL) on engineering design pedagogy and classroom practice. The AI-EDPL software system will use concepts initially pioneered for intelligent tutoring systems, but applied to scaffolding the creation of custom, teacher-made instructional materials that adhere to best practices in design process pedagogy assessment. Unlike many other educational domains, engineering design problems vary widely in scope and solution pathways, which means there will not be a one-size-fits-all tutoring system that can provide feedback to students. This project will examine (a) whether artificial intelligence can support and scaffold teachers in the creation of the necessary models and knowledge structures needed to scaffold and support learners, and, (b) what professional development teachers need to be successful in developing these models. A multi-phased approach will be used, using value-sensitive design processes from the field of human computer interaction to develop minimalist functional systems that can be tested with teachers in classrooms. In order for AI to help teachers, who do not have a lot of time to tinker with software, they must be able to express their intentions in natural language, which must be automatically converted into functional approximations of the task models that can be easily edited. This project will build on best practices in design theory and pedagogy, design documentation, design instruction, design assessment, and AI tutoring to create a one-of-a-kind technology suitable for engineering design instruction at the high school level. It represents a first attempt at providing real-time feedback in a computational setting for an open-ended design challenge, and it does so without marginalizing or diminishing the role of the instructor in the engineering classroom.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.