Educating Generative Designers in Engineering

教育工程领域的生成设计师

• 批准号: 2207408
• 负责人: Zhenghui Sha
• 金额: $ 218.02万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207408&HistoricalAwards=false
• 关键词: Educating; Generative; Designers; Engineering

With support from NSF's Accelerating Discovery program, this project aims to re-envision undergraduate engineering education to include generative design. Generative design is a transformative design technology that uses open-ended artificial intelligence algorithms to arrive at solutions for engineering problems. Generative design software can be freed from preconceived ideas or past solutions. As a result, it allows exploration of a wider variety of potential solutions, with the goal of arriving at an optimal solution in partnership with the human engineer. The proposed project will support the development of open-source educational tools for teaching and learning generative design. These tools will be based on existing computer-assisted design and engineering software, and will include a set of project modules to guide students through authentic design problems. The software and associated design problems will be pilot tested by students at thirteen institutions, including community colleges, Historically Black Colleges and Universities, liberal arts colleges, and public universities. Information from these pilots will be used iteratively to refine the software and teaching approach. This project represents a novel application of artificial intelligence to engineering that could augment the creativity and productivity of the engineering workforce of the future. The overall goal of this project is to facilitate the teaching and learning of generative design at the undergraduate level. To accomplish this goal, the University of Arkansas, the University of Illinois at Urbana-Champaign, Oregon State University, and the Concord Consortium will collaborate to define, implement, and disseminate generative design tools and projects for use in undergraduate courses. Research questions from three perspectives will drive the project: 1) Theoretical perspective: What are the essential elements of generative design thinking that students must acquire so they can work effectively at the human-technology frontier in engineering? 2) Practical perspective: To what extent and in what ways can the curriculum and materials support the learning of generative design as indicated by students' gains in generative design thinking? and 3) Affective perspective: To what extent and in what ways can artificial intelligence affect the professional formation of engineers as indicated by the changes of students' interest and self-efficacy in engineering? To answer these questions, interdisciplinary research that integrates the perspectives and knowledge in engineering design, computer science, learning science, and workforce development will be conducted. The project will involve more than 1,000 students at 13 institutions around the country. The research will include data from demographic surveys, questionnaires, self-efficacy measures, design reports, screencast videos, classroom observations, and participant interviews. The materials developed by the project will be open source, including an open-source tool for teaching and learning generative design and a set of project-based learning modules that guide use of the tool to solve authentic design problems in architectural engineering and energy engineering. The products of this project are expected to equip students with essential skills and mindsets needed to master using artificial intelligence approaches in contemporary engineering practices.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.

在NSF的加速发现计划的支持下，该项目旨在重新启动本科工程教育，以包括生成设计。 生成设计是一种变革性设计技术，它使用开放式人工智能算法来达到工程问题的解决方案。 生成设计软件可以摆脱先入为主的想法或过去的解决方案。 结果，它允许探索更广泛的潜在解决方案，目的是与人类工程师合作达到最佳解决方案。 拟议的项目将支持开发用于教学生成设计的开源教育工具。 这些工具将基于现有的计算机辅助设计和工程软件，并将包括一组项目模块，以指导学生解决真实的设计问题。 该软件和相关的设计问题将由13个机构的学生进行试验，包括社区学院，历史悠久的黑人学院和大学，文科学院和公立大学。 这些飞行员​​的信息将被迭代地用于完善软件和教学方法。 该项目代表了人工智能在工程上的新应用，可以提高未来工程劳动力的创造力和生产力。 该项目的总体目标是促进本科生的生成设计的教学和学习。 为了实现这一目标，阿肯色大学，俄勒冈州立大学Urbana-Champaign大学的伊利诺伊大学和Concord财团将合作定义，实施和传播用于本科课程的生成设计工具和项目。 从三个角度来看，研究问题将推动项目：1）理论观点：生成设计思维的基本要素是学生必须获得的，以便他们可以在工程学的人类技术前沿有效地工作？ 2）实用的观点：按照生成设计思维中学生的成就表明，课程和材料在多大程度上以及在什么方面可以支持学习生成设计？ 3）情感的观点：人工智能在多大程度上以及在什么方面可以影响工程师的专业形成，这是学生在工程学中的兴趣和自我效能的变化所表明的？ 为了回答这些问题，将进行工程设计，计算机科学，学习科学和劳动力发展中的观点和知识的跨学科研究。 该项目将涉及全国13个机构的1,000多名学生。 该研究将包括来自人口调查，问卷调查，自我效能措施，设计报告，屏幕截图视频，课堂观察和参与者访谈的数据。 该项目开发的材料将是开源的，包括用于教学和学习生成设计的开源工具以及一组基于项目的学习模块，可指导使用该工具来解决建筑工程和能源工程中的真实设计问题。 该项目的产品有望为学生提供使用当代工程实践中的人工智能方法来掌握所需的基本技能和思维方式。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估的值得支持的。

项目成果

Design representation for performance evaluation of 3D shapes in structure-aware generative design

• DOI: 10.1017/dsj.2023.25
• 发表时间: 2023-09
• 期刊: Design Science
• 影响因子: 2.4
• 作者: Xingang Li;Charles Xie;Z. Sha

Beyond Solar Cookers: Modeling and Designing Concentrated Solar Power as Engineering Projects in Physics Classrooms

超越太阳能炊具：将聚光太阳能建模和设计为物理课堂的工程项目

• DOI: 10.1119/5.0090548
• 发表时间: 2023
• 期刊: The Physics Teacher
• 作者: Xie, Charles

Human-Centered Generative Design Framework: An Early Design Framework to Support Concept Creation and Evaluation

以人为本的生成设计框架：支持概念创建和评估的早期设计框架

• DOI: 10.1080/10447318.2023.2171489
• 发表时间: 2023
• 期刊: International Journal of Human–Computer Interaction
• 作者: Demirel, H. Onan;Goldstein, Molly H.;Li, Xingang;Sha, Zhenghui
• 通讯作者: Sha, Zhenghui

Deep Learning of Cross-Modal Tasks for Conceptual Design of Engineered Products: A Review

• DOI: 10.1115/detc2022-90696
• 发表时间: 2022-08
• 期刊: Volume 6: 34th International Conference on Design Theory and Methodology (DTM)
• 作者: Xingang Li;Ye Wang;Z. Sha

Exploring Generative Design Thinking for Engineering Design and Design Education

探索工程设计与设计教育的生成设计思维

• 发表时间: 2021
• 期刊: 2021 ASEE Midwest Section Conference
• 作者: Li, Xingang;Demirel, H. Onan;Goldstein, Molly H.;Sha, Zhenghui
• 通讯作者: Sha, Zhenghui