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专业的几乎所有方面。生成AI（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交流，设计，编码和教学，创造效率和潜在的新机会的方式，尤其是为WHOMINE的新机会，尤其可以降低障碍。鉴于这些技术对工程专业的潜在影响，工程教育者必须了解GAI并能够有效地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社区的影响，同时开发和评估方法以及支持这些技术在研究和教学实践中的采用和集成的资源。通过此，该项目将支持未来工程师和劳动力发展的形成。此外，鉴于GAI的使用也引起了需要解决的新道德挑战，这项工作还将评估工程教育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，或在教学中，用于计划课程或练习。通过帮助工程教育社区以最佳方式拥抱GAI，该项目将使各种利益相关者能够利用GAI。教育工作者将利用该项目的发现，以改善工程教育研究和教学，并对当前和未来的工程师的准备更广泛影响。通过文献综述以及在线数据收集和分析的结合，该项目将确定工程教育研究和教学如何有可能纳入基于GAI的应用程序。以下具体研究问题将指导该项目：1）当前如何将GAI用于相关领域的研究和教学，例如计算和STEM教育，以及对工程教育的影响？ 2）工程教育者对研究和教学活动的目前对GAI的认识是什么？ 3）哪些用例和场景可以有助于对GAI进行工程教育研究和教学的有效和负责任？为了解决这些问题，将与研究人员和教育工作者进行协作共同设计研讨会。通过这些研讨会，将开发和评估用于在工程教育中使用GAI的方案。下一步，将要求研究人员和讲师将场景纳入其工作流程中，以便可以进一步评估其可用性和实用性。使用用户提供的反馈，该项目将创建并自由传播一套为从事工程教育研究或教授工程学的人设计的方案和准则。通过该项目，社区还将通过与GAI在工程教育中使用的对话和辩论的机会受益。这项工作将为建立社会技术基础设施奠定基础，以促进在工程教育中对GAI的有效和负责任使用。鉴于GAI有可能塑造工程教育的潜力，该项目可能是一个潜在的更大项目的第一次迭代。最后，该项目还将通过确定使用GAIS和相关技术的障碍来做出贡献，以及这可能如何限制更具包容性的工程教育和概述指南的目标，以实现该技术的负责任和道德使用。该奖项反映了NSF的法定任务，并具有使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来支持。