EAGER: Impact of Generative Artificial Intelligence (GAI) on Engineering Education Practices

EAGER：生成人工智能 (GAI) 对工程教育实践的影响

• 批准号: 2319137
• 负责人: Aditya Johri
• 金额: $ 30万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319137&HistoricalAwards=false
• 关键词: EAGER; Impact; Generative; Artificial; Intelligence

Advances in Artificial Intelligence (AI) are affecting almost all aspects of the engineering profession. Recent developments in Generative AI (GAI) and the popularity of technologies like Grammarly, ChatGPT, GitHub CoPilot are changing the way that engineers communicate, design, code, and teach, creating efficiencies and potentially new opportunities, particularly for marginalized individuals for whom GAI technologies can lower barriers. Given the potential impact of these technologies on the engineering profession, it is imperative that engineering educators are both aware of GAI and able to use it productively for teaching and for engineering education research. This project will explore the impact of GAI technologies on the engineering research and teaching community while developing and evaluating methods as well as resources for supporting the adoption and integration of these technologies in research and teaching practices. Through this, the project will support the formation of future engineers and workforce development. In addition, given that the use of GAI is also giving rise to novel ethical challenges that need to be addressed, this work will also evaluate how engineering educators can use GAI responsibly. Whether in research, for data analysis and writing, and idea generation, or in teaching, for planning lessons or exercises. By helping the engineering education community embrace GAI in optimal ways this project will allow diverse stakeholders to take advantage of GAI. Findings from this project will be leveraged by educators to improve engineering education research and teaching with broader impact on the preparation of current and future engineers. Through a combination of literature review and online data collection and analysis, the project will identify how engineering education research and teaching can potentially incorporate GAI based applications. The following specific research questions will guide the project: 1) How is GAI currently being used for research and teaching in related fields, such as computing and STEM education, and what are the implications for engineering education? 2) What is the current awareness of GAI among engineering educators in relation to their research and teaching activities? 3) What use cases and scenarios can assist with effective and responsible use of GAI for engineering education research and teaching? To address these questions, collaborative co-design workshops with researchers and educators will be undertaken. Through these workshops, scenarios for the use of GAI within engineering education will be developed and evaluated. As a next step, researchers and instructors will be asked to incorporate the scenarios into their workflow so that their usability and utility can be further assessed. Using the feedback provided by the users, the project will create and freely disseminate a set of scenarios and guidelines designed for those who do engineering education research or teach engineering. Through this project, the community will also benefit through opportunities for dialogues and debates related to the use of GAI in engineering education. This work will lay the foundation for building socio-technical infrastructure essential for facilitating effective and responsible use of GAI within engineering education. Given the potential for GAI to shape engineering education, this project can be the first iteration of a potentially larger project. Finally, the project will also contribute by identifying barriers to the use of GAIs and related technology and how that might restrict the goals of more inclusive engineering education and outline guidelines for responsible and ethical use of the technology.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.

人工智能 (AI) 的进步正在影响工程行业的几乎所有方面。生成式人工智能 (GAI) 的最新发展以及 Grammarly、ChatGPT、GitHub CoPilot 等技术的普及正在改变工程师沟通、设计、编码和教学的方式，创造效率和潜在的新机会，特别是对于使用 GAI 技术的边缘化个人而言可以降低门槛。考虑到这些技术对工程专业的潜在影响，工程教育工作者必须了解 GAI 并能够有效地将其用于教学和工程教育研究。该项目将探讨 GAI 技术对工程研究和教学界的影响，同时开发和评估支持这些技术在研究和教学实践中采用和集成的方法和资源。通过这一点，该项目将支持未来工程师的形成和劳动力的发展。此外，鉴于 GAI 的使用也引发了需要解决的新的伦理挑战，这项工作还将评估工程教育工作者如何负责任地使用 GAI。无论是在研究中，用于数据分析和写作、创意生成，还是在教学中，用于规划课程或练习。通过帮助工程教育界以最佳方式拥抱 GAI，该项目将使不同的利益相关者能够利用 GAI。教育工作者将利用该项目的研究成果来改善工程教育研究和教学，对当前和未来工程师的培养产生更广泛的影响。通过结合文献综述和在线数据收集和分析，该项目将确定工程教育研究和教学如何能够潜在地纳入基于 GAI 的应用程序。以下具体研究问题将指导该项目：1）GAI 目前如何用于相关领域（例如计算和 STEM 教育）的研究和教学，对工程教育有何影响？ 2) 目前工程教育工作者在研究和教学活动中对 GAI 的认识如何？ 3) 哪些用例和场景可以帮助有效且负责任地使用 GAI 进行工程教育研究和教学？为了解决这些问题，将与研究人员和教育工作者合作举办共同设计研讨会。通过这些研讨会，将开发和评估在工程教育中使用 GAI 的场景。下一步，研究人员和讲师将被要求将这些场景纳入他们的工作流程中，以便进一步评估其可用性和实用性。根据用户提供的反馈，该项目将创建并免费传播一套为工程教育研究或工程教学人员设计的场景和指南。通过这个项目，社区还将通过与 GAI 在工程教育中的使用相关的对话和辩论机会而受益。这项工作将为建设社会技术基础设施奠定基础，这对于促进工程教育中有效和负责任地使用 GAI 至关重要。鉴于 GAI 塑造工程教育的潜力，该项目可能是潜在更大项目的第一次迭代。最后，该项目还将通过确定使用 GAI 和相关技术的障碍以及这些障碍如何限制更具包容性的工程教育的目标来做出贡献，并概述负责任和合乎道德地使用该技术的指南。该奖项反映了 NSF 的法定使命，并具有通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。